diff --git a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
index d49ecd847..24746482b 100644
--- a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
@@ -3054,259 +3054,6 @@ Output series:
 +-----------------------------+--------------------------------------------------------+
 ```
 
-
-### 4.8 MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:**  MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### 4.9 MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:**  MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                   true|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## 5. Frequency Domain Analysis
 
 ### 5.1 Conv
@@ -4823,192 +4570,6 @@ Output series:
 +-----------------------------+-------------------------------------------------+
 ```
 
-### 7.4 MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average;  **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of  the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used. 
-
-Input series:
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## 8. Series Discovery
 
 ### 8.1 ConsecutiveSequences
@@ -5263,127 +4824,3 @@ Output Series:
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### 9.2 Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
index d317569c2..87e18e973 100644
--- a/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
@@ -3054,259 +3054,6 @@ Output series:
 +-----------------------------+--------------------------------------------------------+
 ```
 
-
-### 4.8 MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:**  MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### 4.9 MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:**  MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                   true|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## 5. Frequency Domain Analysis
 
 ### 5.1 Conv
@@ -4882,192 +4629,6 @@ Output series:
 +-----------------------------+-------------------------------------------------+
 ```
 
-### 7.4 MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average;  **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of  the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used. 
-
-Input series:
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## 8. Series Discovery
 
 ### 8.1 ConsecutiveSequences
@@ -5323,126 +4884,3 @@ Output Series:
 +-----------------------------+---------------------------+
 ```
 
-### 9.2 Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
index 722a30b52..8f467de61 100644
--- a/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
@@ -3057,259 +3057,6 @@ Output series:
 +-----------------------------+--------------------------------------------------------+
 ```
 
-
-### MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:**  MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:**  MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                   true|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## Frequency Domain Analysis
 
 ### Conv
@@ -4824,192 +4571,6 @@ Output series:
 +-----------------------------+-------------------------------------------------+
 ```
 
-### MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average;  **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of  the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used. 
-
-Input series:
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## Series Discovery
 
 ### ConsecutiveSequences
@@ -5265,126 +4826,3 @@ Output Series:
 +-----------------------------+---------------------------+
 ```
 
-### Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
index 5289bde10..8eca6741f 100644
--- a/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
@@ -3056,259 +3056,6 @@ Output series:
 +-----------------------------+--------------------------------------------------------+
 ```
 
-
-### MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:**  MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:**  MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                   true|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## Frequency Domain Analysis
 
 ### Conv
@@ -4883,192 +4630,6 @@ Output series:
 +-----------------------------+-------------------------------------------------+
 ```
 
-### MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average;  **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of  the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used. 
-
-Input series:
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## Series Discovery
 
 ### ConsecutiveSequences
@@ -5324,126 +4885,4 @@ Output Series:
 +-----------------------------+---------------------------+
 ```
 
-### Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
 
diff --git a/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
index 8985f53e9..0e7598612 100644
--- a/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
@@ -3057,259 +3057,6 @@ Output series:
 +-----------------------------+--------------------------------------------------------+
 ```
 
-
-### MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:**  MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:**  MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                   true|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## Frequency Domain Analysis
 
 ### Conv
@@ -4825,192 +4572,6 @@ Output series:
 +-----------------------------+-------------------------------------------------+
 ```
 
-### MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average;  **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of  the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used. 
-
-Input series:
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## Series Discovery
 
 ### ConsecutiveSequences
@@ -5266,126 +4827,3 @@ Output Series:
 +-----------------------------+---------------------------+
 ```
 
-### Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
index 5289bde10..abe6fb262 100644
--- a/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
@@ -3056,259 +3056,6 @@ Output series:
 +-----------------------------+--------------------------------------------------------+
 ```
 
-
-### MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:**  MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:**  MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                   true|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## Frequency Domain Analysis
 
 ### Conv
@@ -4883,192 +4630,6 @@ Output series:
 +-----------------------------+-------------------------------------------------+
 ```
 
-### MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average;  **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of  the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used. 
-
-Input series:
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## Series Discovery
 
 ### ConsecutiveSequences
@@ -5323,127 +4884,3 @@ Output Series:
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
index d49ecd847..24746482b 100644
--- a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
@@ -3054,259 +3054,6 @@ Output series:
 +-----------------------------+--------------------------------------------------------+
 ```
 
-
-### 4.8 MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:**  MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### 4.9 MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:**  MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                   true|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## 5. Frequency Domain Analysis
 
 ### 5.1 Conv
@@ -4823,192 +4570,6 @@ Output series:
 +-----------------------------+-------------------------------------------------+
 ```
 
-### 7.4 MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average;  **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of  the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used. 
-
-Input series:
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## 8. Series Discovery
 
 ### 8.1 ConsecutiveSequences
@@ -5263,127 +4824,3 @@ Output Series:
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### 9.2 Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
index d317569c2..87e18e973 100644
--- a/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
@@ -3054,259 +3054,6 @@ Output series:
 +-----------------------------+--------------------------------------------------------+
 ```
 
-
-### 4.8 MasterTrain
-
-#### Usage
-
-This function is used to train the VAR model based on master data. The model is trained on learning samples consisting of p+1 consecutive non-error points.
-
-**Name:**  MasterTrain
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterTrain as 'org.apache.iotdb.library.anomaly.UDTFMasterTrain'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-SQL for query:
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-```
-
-### 4.9 MasterDetect
-
-#### Usage
-
-This function is used to detect time series and repair errors based on master data. The VAR model is trained by MasterTrain.
-
-**Name:**  MasterDetect
-
-**Input Series：** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `p`: The order of the model.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple distance of the k-th nearest neighbor in the master data.
-+ `eta`: The distance threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `eta`: The detection threshold. By default, it will be estimated based on the 3-sigma rule.
-+ `output_type`: The type of output. 'repair' for repairing and 'anomaly' for anomaly detection.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input.
-
-**Installation**
-- Install IoTDB from branch `research/master-detector`.
-- Run `mvn spotless:apply`.
-- Run `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies`.
-- Copy `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar` to `./ext/udf/`.
-- Start IoTDB server and run `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'` in client.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### Repairing
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### Anomaly Detection
-
-SQL for query:
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-Output series:
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                   true|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## 5. Frequency Domain Analysis
 
 ### 5.1 Conv
@@ -4882,192 +4629,6 @@ Output series:
 +-----------------------------+-------------------------------------------------+
 ```
 
-### 7.4 MasterRepair
-
-#### Usage
-
-This function is used to clean time series with master data.
-
-**Name**: MasterRepair
-**Input Series:** Support multiple input series. The types are are in INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `omega`: The window size. It is a non-negative integer whose unit is millisecond. By default, it will be estimated according to the distances of two tuples with various time differences.
-+ `eta`: The distance threshold. It is a positive number. By default, it will be estimated according to the distance distribution of tuples in windows.
-+ `k`: The number of neighbors in master data. It is a positive integer. By default, it will be estimated according to the tuple dis- tance of the k-th nearest neighbor in the master data.
-+ `output_column`: The repaired column to output, defaults to 1 which means output the repair result of the first column.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-#### Examples
-
-Input series:
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-SQL for query:
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-Output series:
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### Usage
-This function is used to repair the value of the seasonal time series via decomposition. Currently, two methods are supported: **Classical** - detect irregular fluctuations through residual component decomposed by classical decomposition, and repair them through moving average;  **Improved** - detect irregular fluctuations through residual component decomposed by improved decomposition, and repair them through moving median.
-
-**Name:** SEASONALREPAIR
-
-**Input Series:** Only support a single input series. The data type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:**
-
-+ `method`: The decomposition method used to repair, which is 'Classical' or 'Improved'. By default, classical decomposition is used.
-+ `period`: It is the period of  the time series.
-+ `k`: It is the range threshold of residual term, which limits the degree to which the residual term is off-center. By default, it is 9.
-+ `max_iter`: It is the maximum number of iterations for the algorithm. By default, it is 10.
-
-**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-
-**Note:** `NaN` will be filled with linear interpolation before repairing.
-
-#### Examples
-
-##### Repair with Classical
-
-When `method` is 'Classical' or default value, classical decomposition method is used. 
-
-Input series:
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-SQL for query:
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### Repair with Improved
-When `method` is 'Improved', improved decomposition method is used.
-
-Input series is the same as above, the SQL for query is shown below:
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-Output series:
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-​    Licensed to the Apache Software Foundation (ASF) under one
-​    or more contributor license agreements.  See the NOTICE file
-​    distributed with this work for additional information
-​    regarding copyright ownership.  The ASF licenses this file
-​    to you under the Apache License, Version 2.0 (the
-​    "License"); you may not use this file except in compliance
-​    with the License.  You may obtain a copy of the License at
-​    
-​        http://www.apache.org/licenses/LICENSE-2.0
-​    
-​    Unless required by applicable law or agreed to in writing,
-​    software distributed under the License is distributed on an
-​    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-​    KIND, either express or implied.  See the License for the
-​    specific language governing permissions and limitations
-​    under the License.
-
--->
-
 ## 8. Series Discovery
 
 ### 8.1 ConsecutiveSequences
@@ -5323,126 +4884,3 @@ Output Series:
 +-----------------------------+---------------------------+
 ```
 
-### 9.2 Representation
-
-#### Usage
-
-This function is used to represent a time series.
-
-**Name:** Representation
-
-**Input Series:** Only support a single input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is INT32. The length is `tb*vb`. The timestamps starting from 0 only indicate the order.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### Usage
-
-This function is used to calculate the matching score of two time series according to the representation.
-
-**Name:** RM
-
-**Input Series:** Only support two input numeric series. The type is INT32 / INT64 / FLOAT / DOUBLE.
-
-**Parameters:** 
-
-- `tb`: The number of timestamp blocks. Its default value is 10.
-- `vb`: The number of value blocks. Its default value is 10.
-
-**Output Series:** Output a single series. The type is DOUBLE. There is only one data point in the series, whose timestamp is 0 and value is the matching score.
-
-**Note:** 
-
-- Parameters `tb` and `vb` should be positive integers.
-
-#### Examples
-
-##### Assigning Window Size and Dimension
-
-Input Series: 
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-SQL for query: 
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-Output Series:
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md b/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
index d33ad35f7..b03ffe964 100644
--- a/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_apache.md
@@ -3110,262 +3110,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
 +-----------------------------+--------------------------------------------------------+
 ```
 
-### 4.8 MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### 4.9 MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                  false|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 5. 频域分析
 
 ### 5.1 Conv
@@ -4927,191 +4671,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
 +-----------------------------+-------------------------------------------------+
 ```
 
-### 7.4 MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：** 
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 8. 序列发现
 
 ### 8.1 ConsecutiveSequences
@@ -5366,127 +4925,3 @@ select ar(s0,"p"="2") from root.test.d0
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### 9.2 Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:**  Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:**  RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md b/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
index a1283b5b3..60be7891c 100644
--- a/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/zh/UserGuide/Master/Tree/SQL-Manual/UDF-Libraries_timecho.md
@@ -3110,262 +3110,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
 +-----------------------------+--------------------------------------------------------+
 ```
 
-### 4.8 MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### 4.9 MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                  false|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 5. 频域分析
 
 ### 5.1 Conv
@@ -4915,191 +4659,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
 +-----------------------------+-------------------------------------------------+
 ```
 
-### 7.4 MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：** 
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 8. 序列发现
 
 ### 8.1 ConsecutiveSequences
@@ -5354,127 +4913,3 @@ select ar(s0,"p"="2") from root.test.d0
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### 9.2 Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:**  Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:**  RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md b/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
index bc7d45871..4f20952fd 100644
--- a/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_apache.md
@@ -3110,262 +3110,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
 +-----------------------------+--------------------------------------------------------+
 ```
 
-### MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                  false|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 频域分析
 
 ### Conv
@@ -4928,191 +4672,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
 +-----------------------------+-------------------------------------------------+
 ```
 
-### MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：** 
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 序列发现
 
 ### ConsecutiveSequences
@@ -5367,127 +4926,3 @@ select ar(s0,"p"="2") from root.test.d0
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:**  Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:**  RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md b/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
index cdf8428e0..8c621861b 100644
--- a/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/zh/UserGuide/V1.3.x/SQL-Manual/UDF-Libraries_timecho.md
@@ -3110,262 +3110,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
 +-----------------------------+--------------------------------------------------------+
 ```
 
-### MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                  false|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 频域分析
 
 ### Conv
@@ -4914,191 +4658,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
 +-----------------------------+-------------------------------------------------+
 ```
 
-### MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：** 
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 序列发现
 
 ### ConsecutiveSequences
@@ -5353,127 +4912,3 @@ select ar(s0,"p"="2") from root.test.d0
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:**  Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:**  RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md b/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
index 0db5452fa..563678ca7 100644
--- a/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_apache.md
@@ -3110,262 +3110,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
 +-----------------------------+--------------------------------------------------------+
 ```
 
-### MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                  false|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 频域分析
 
 ### Conv
@@ -4927,191 +4671,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
 +-----------------------------+-------------------------------------------------+
 ```
 
-### MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：** 
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 序列发现
 
 ### ConsecutiveSequences
@@ -5277,26 +4836,6 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1
 +-----------------------------+--------------------------------------------------------------------+
 ```
 
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
 
 ## 机器学习
 
@@ -5366,127 +4905,3 @@ select ar(s0,"p"="2") from root.test.d0
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:**  Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:**  RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md b/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
index cdf8428e0..ddb3d8d99 100644
--- a/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/zh/UserGuide/dev-1.3/SQL-Manual/UDF-Libraries_timecho.md
@@ -3110,262 +3110,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
 +-----------------------------+--------------------------------------------------------+
 ```
 
-### MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                  false|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 频域分析
 
 ### Conv
@@ -4914,190 +4658,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
 +-----------------------------+-------------------------------------------------+
 ```
 
-### MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：** 
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
 
 ## 序列发现
 
@@ -5264,27 +4824,6 @@ select consecutivewindows(s1,s2,'length'='10m') from root.test.d1
 +-----------------------------+--------------------------------------------------------------------+
 ```
 
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 机器学习
 
 ### AR
@@ -5353,127 +4892,3 @@ select ar(s0,"p"="2") from root.test.d0
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:**  Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:**  RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
index d33ad35f7..b03ffe964 100644
--- a/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
+++ b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_apache.md
@@ -3110,262 +3110,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
 +-----------------------------+--------------------------------------------------------+
 ```
 
-### 4.8 MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### 4.9 MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                  false|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 5. 频域分析
 
 ### 5.1 Conv
@@ -4927,191 +4671,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
 +-----------------------------+-------------------------------------------------+
 ```
 
-### 7.4 MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：** 
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 8. 序列发现
 
 ### 8.1 ConsecutiveSequences
@@ -5366,127 +4925,3 @@ select ar(s0,"p"="2") from root.test.d0
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### 9.2 Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:**  Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:**  RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-
diff --git a/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
index a1283b5b3..60be7891c 100644
--- a/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
+++ b/src/zh/UserGuide/latest/SQL-Manual/UDF-Libraries_timecho.md
@@ -3110,262 +3110,6 @@ select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
 +-----------------------------+--------------------------------------------------------+
 ```
 
-### 4.8 MasterTrain
-
-#### 函数简介
-
-本函数基于主数据训练VAR预测模型。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由连续p+1个非错误值作为训练样本训练VAR模型，输出训练后的模型参数。
-
-**函数名：** MasterTrain
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-
-**输出序列：** 输出单个序列，类型为DOUBLE。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterTrain as org.apache.iotdb.library.anomaly.UDTFMasterTrain'`。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|
-+-----------------------------+------------+------------+--------------+--------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|
-+-----------------------------+------------+------------+--------------+--------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|                         Time|MasterTrain(root.test.lo, root.test.la, root.test.m_lo, root.test.m_la, "p"="3", "eta"="1.0")|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                          0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00|                                                                           0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00|                                                                          0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00|                                                                          -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00|                                                                          0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00|                                                                           1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|                                                                          0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00|                                                                         -0.07863708480736269|
-+-----------------------------+---------------------------------------------------------------------------------------------+
-
-```
-
-### 4.9 MasterDetect
-
-#### 函数简介
-
-本函数基于主数据检测并修复时间序列中的错误值。将根据提供的主数据判断时间序列中的数据点是否为错误值，并由MasterTrain训练的模型进行时间序列预测，错误值将由预测值及主数据共同修复。
-
-**函数名：** MasterDetect
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-+ `p`:模型阶数。
-+ `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-+ `eta`:错误值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `beta`:异常值判定阈值，在缺省情况下，算法根据3-sigma原则自动估计该参数。
-+ `output_type`:输出结果类型，可选'repair'或'anomaly'，即输出修复结果或异常检测结果，在缺省情况下默认为'repair'。
-+ `output_column`：输出列的序号，默认为1，即输出第一列的修复结果。
-
-**安装方式：**
-
-- 从IoTDB代码仓库下载`research/master-detector`分支代码到本地
-- 在根目录运行 `mvn spotless:apply`
-- 在根目录运行 `mvn clean package -pl library-udf -DskipTests -am -P get-jar-with-dependencies` 编译项目
-- 将 `./library-UDF/target/library-udf-1.2.0-SNAPSHOT-jar-with-dependencies.jar`复制到IoTDB服务器的`./ext/udf/` 路径下。
-- 启动 IoTDB服务器，在客户端中执行 `create function MasterDetect as 'org.apache.iotdb.library.anomaly.UDTFMasterDetect'`。
-
-**输出序列：** 输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|                         Time|root.test.lo|root.test.la|root.test.m_la|root.test.m_lo|     root.test.model|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-|1970-01-01T08:00:00.001+08:00| 39.99982556|  116.327274|   116.3271939|   39.99984748| 0.13656607660463288|
-|1970-01-01T08:00:00.002+08:00| 39.99983865|  116.327305|   116.3272269|   39.99984748|  0.8291884323013894|
-|1970-01-01T08:00:00.003+08:00| 40.00019038| 116.3273291|   116.3272634|   39.99984769| 0.05012816073171693|
-|1970-01-01T08:00:00.004+08:00| 39.99982556|  116.327342|   116.3273015|    39.9998483| -0.5495287787485761|
-|1970-01-01T08:00:00.005+08:00| 39.99982991| 116.3273744|    116.327339|   39.99984892| 0.03740486307345578|
-|1970-01-01T08:00:00.006+08:00| 39.99982716| 116.3274117|   116.3273759|   39.99984892|  1.0500132150475212|
-|1970-01-01T08:00:00.007+08:00|  39.9998259| 116.3274396|   116.3274163|   39.99984953| 0.04583944643116993|
-|1970-01-01T08:00:00.008+08:00| 39.99982597| 116.3274668|   116.3274525|   39.99985014|-0.07863708480736269|
-|1970-01-01T08:00:00.009+08:00| 39.99982226| 116.3275026|   116.3274915|   39.99985076|                null|
-|1970-01-01T08:00:00.010+08:00| 39.99980988| 116.3274967|   116.3275235|   39.99985137|                null|
-|1970-01-01T08:00:00.011+08:00| 39.99984873| 116.3274929|   116.3275611|   39.99985199|                null|
-|1970-01-01T08:00:00.012+08:00| 39.99981589| 116.3274745|   116.3275974|    39.9998526|                null|
-|1970-01-01T08:00:00.013+08:00|  39.9998259| 116.3275095|   116.3276338|   39.99985384|                null|
-|1970-01-01T08:00:00.014+08:00| 39.99984873| 116.3274787|   116.3276695|   39.99985446|                null|
-|1970-01-01T08:00:00.015+08:00|  39.9998343| 116.3274693|   116.3277045|   39.99985569|                null|
-|1970-01-01T08:00:00.016+08:00| 39.99983316| 116.3274941|   116.3277389|   39.99985631|                null|
-|1970-01-01T08:00:00.017+08:00| 39.99983311| 116.3275401|   116.3277747|   39.99985693|                null|
-|1970-01-01T08:00:00.018+08:00| 39.99984113| 116.3275713|   116.3278041|   39.99985756|                null|
-|1970-01-01T08:00:00.019+08:00| 39.99983602| 116.3276003|   116.3278379|   39.99985818|                null|
-|1970-01-01T08:00:00.020+08:00|  39.9998355| 116.3276308|   116.3278723|    39.9998588|                null|
-|1970-01-01T08:00:00.021+08:00| 40.00012176| 116.3276107|   116.3279026|   39.99985942|                null|
-|1970-01-01T08:00:00.022+08:00|  39.9998404| 116.3276684|          null|          null|                null|
-|1970-01-01T08:00:00.023+08:00| 39.99983942| 116.3277016|          null|          null|                null|
-|1970-01-01T08:00:00.024+08:00| 39.99984113| 116.3277284|          null|          null|                null|
-|1970-01-01T08:00:00.025+08:00| 39.99984283| 116.3277562|          null|          null|                null|
-+-----------------------------+------------+------------+--------------+--------------+--------------------+
-```
-
-##### 修复
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+--------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                            116.327274|
-|1970-01-01T08:00:00.002+08:00|                                                                            116.327305|
-|1970-01-01T08:00:00.003+08:00|                                                                           116.3273291|
-|1970-01-01T08:00:00.004+08:00|                                                                            116.327342|
-|1970-01-01T08:00:00.005+08:00|                                                                           116.3273744|
-|1970-01-01T08:00:00.006+08:00|                                                                           116.3274117|
-|1970-01-01T08:00:00.007+08:00|                                                                           116.3274396|
-|1970-01-01T08:00:00.008+08:00|                                                                           116.3274668|
-|1970-01-01T08:00:00.009+08:00|                                                                           116.3275026|
-|1970-01-01T08:00:00.010+08:00|                                                                           116.3274967|
-|1970-01-01T08:00:00.011+08:00|                                                                           116.3274929|
-|1970-01-01T08:00:00.012+08:00|                                                                           116.3274745|
-|1970-01-01T08:00:00.013+08:00|                                                                           116.3275095|
-|1970-01-01T08:00:00.014+08:00|                                                                           116.3274787|
-|1970-01-01T08:00:00.015+08:00|                                                                           116.3274693|
-|1970-01-01T08:00:00.016+08:00|                                                                           116.3274941|
-|1970-01-01T08:00:00.017+08:00|                                                                           116.3275401|
-|1970-01-01T08:00:00.018+08:00|                                                                           116.3275713|
-|1970-01-01T08:00:00.019+08:00|                                                                           116.3276003|
-|1970-01-01T08:00:00.020+08:00|                                                                           116.3276308|
-|1970-01-01T08:00:00.021+08:00|                                                                           116.3276338|
-|1970-01-01T08:00:00.022+08:00|                                                                           116.3276684|
-|1970-01-01T08:00:00.023+08:00|                                                                           116.3277016|
-|1970-01-01T08:00:00.024+08:00|                                                                           116.3277284|
-|1970-01-01T08:00:00.025+08:00|                                                                           116.3277562|
-+-----------------------------+--------------------------------------------------------------------------------------+
-```
-
-##### 异常检测
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test
-```
-
-输出序列：
-
-```
-+-----------------------------+---------------------------------------------------------------------------------------+
-|                         Time|MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0')|
-+-----------------------------+---------------------------------------------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                                                  false|
-|1970-01-01T08:00:00.002+08:00|                                                                                  false|
-|1970-01-01T08:00:00.003+08:00|                                                                                  false|
-|1970-01-01T08:00:00.004+08:00|                                                                                  false|
-|1970-01-01T08:00:00.005+08:00|                                                                                   true|
-|1970-01-01T08:00:00.006+08:00|                                                                                  false|
-|1970-01-01T08:00:00.007+08:00|                                                                                  false|
-|1970-01-01T08:00:00.008+08:00|                                                                                  false|
-|1970-01-01T08:00:00.009+08:00|                                                                                  false|
-|1970-01-01T08:00:00.010+08:00|                                                                                  false|
-|1970-01-01T08:00:00.011+08:00|                                                                                  false|
-|1970-01-01T08:00:00.012+08:00|                                                                                  false|
-|1970-01-01T08:00:00.013+08:00|                                                                                  false|
-|1970-01-01T08:00:00.014+08:00|                                                                                   true|
-|1970-01-01T08:00:00.015+08:00|                                                                                  false|
-|1970-01-01T08:00:00.016+08:00|                                                                                  false|
-|1970-01-01T08:00:00.017+08:00|                                                                                  false|
-|1970-01-01T08:00:00.018+08:00|                                                                                  false|
-|1970-01-01T08:00:00.019+08:00|                                                                                  false|
-|1970-01-01T08:00:00.020+08:00|                                                                                  false|
-|1970-01-01T08:00:00.021+08:00|                                                                                  false|
-|1970-01-01T08:00:00.022+08:00|                                                                                  false|
-|1970-01-01T08:00:00.023+08:00|                                                                                  false|
-|1970-01-01T08:00:00.024+08:00|                                                                                  false|
-|1970-01-01T08:00:00.025+08:00|                                                                                  false|
-+-----------------------------+---------------------------------------------------------------------------------------+
-```
-
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 5. 频域分析
 
 ### 5.1 Conv
@@ -4915,191 +4659,6 @@ select valuerepair(s1,'method'='LsGreedy') from root.test.d2
 +-----------------------------+-------------------------------------------------+
 ```
 
-### 7.4 MasterRepair
-
-#### 函数简介
-
-本函数实现基于主数据的时间序列数据修复。
-
-**函数名：**MasterRepair
-
-**输入序列：** 支持多个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：**
-
-- `omega`：算法窗口大小，非负整数（单位为毫秒）， 在缺省情况下，算法根据不同时间差下的两个元组距离自动估计该参数。
-- `eta`：算法距离阈值，正数， 在缺省情况下，算法根据窗口中元组的距离分布自动估计该参数。
-- `k`：主数据中的近邻数量，正整数， 在缺省情况下，算法根据主数据中的k个近邻的元组距离自动估计该参数。
-- `output_column`：输出列的序号，默认输出第一列的修复结果。
-
-**输出序列：**输出单个序列，类型与输入数据中对应列的类型相同，序列为输入列修复后的结果。
-
-#### 使用示例
-
-输入序列：
-
-```
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|                         Time|root.test.t1|root.test.t2|root.test.t3|root.test.m1|root.test.m2|root.test.m3|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-|2021-07-01T12:00:01.000+08:00|        1704|     1154.55|       0.195|        1704|     1154.55|       0.195|
-|2021-07-01T12:00:02.000+08:00|        1702|     1152.30|       0.193|        1702|     1152.30|       0.193|
-|2021-07-01T12:00:03.000+08:00|        1702|     1148.65|       0.192|        1702|     1148.65|       0.192|
-|2021-07-01T12:00:04.000+08:00|        1701|     1145.20|       0.194|        1701|     1145.20|       0.194|
-|2021-07-01T12:00:07.000+08:00|        1703|     1150.55|       0.195|        1703|     1150.55|       0.195|
-|2021-07-01T12:00:08.000+08:00|        1694|     1151.55|       0.193|        1704|     1151.55|       0.193|
-|2021-07-01T12:01:09.000+08:00|        1705|     1153.55|       0.194|        1705|     1153.55|       0.194|
-|2021-07-01T12:01:10.000+08:00|        1706|     1152.30|       0.190|        1706|     1152.30|       0.190|
-+-----------------------------+------------+------------+------------+------------+------------+------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
-```
-
-输出序列：
-
-
-```
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|                         Time|MasterRepair(root.test.t1,root.test.t2,root.test.t3,root.test.m1,root.test.m2,root.test.m3)|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-|2021-07-01T12:00:01.000+08:00|                                                                                       1704|
-|2021-07-01T12:00:02.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:03.000+08:00|                                                                                       1702|
-|2021-07-01T12:00:04.000+08:00|                                                                                       1701|
-|2021-07-01T12:00:07.000+08:00|                                                                                       1703|
-|2021-07-01T12:00:08.000+08:00|                                                                                       1704|
-|2021-07-01T12:01:09.000+08:00|                                                                                       1705|
-|2021-07-01T12:01:10.000+08:00|                                                                                       1706|
-+-----------------------------+-------------------------------------------------------------------------------------------+
-```
-<!--
-### 7.5 SeasonalRepair
-
-#### 函数简介
-本函数用于对周期性时间序列的数值进行基于分解的修复。目前，本函数支持两种方法：**Classical**使用经典分解方法得到的残差项检测数值的异常波动，并使用滑动平均修复序列；**Improved**使用改进的分解方法得到的残差项检测数值的异常波动，并使用滑动中值修复序列。
-
-**函数名：** SEASONALREPAIR
-
-**输入序列：** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数：** 
-
-+ `method`：修复时采用的分解方法，取值为'Classical'或'Improved'。在缺省情况下，使用经典分解方法进行修复。
-+ `period`：序列的周期。
-+ `k`：残差项的范围阈值，用来限制残差项偏离中心的程度。在缺省情况下为9。
-+ `max_iter`：算法的最大迭代次数。在缺省情况下为10。
-
-**输出序列：** 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。
-
-**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-
-#### 使用示例
-##### 使用经典分解方法进行修复
-当`method`缺省或取值为'Classical'时，本函数将使用经典分解方法进行数值修复。
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d2.s1|
-+-----------------------------+---------------+
-|2020-01-01T00:00:02.000+08:00|          100.0|
-|2020-01-01T00:00:04.000+08:00|          120.0|
-|2020-01-01T00:00:06.000+08:00|           80.0|
-|2020-01-01T00:00:08.000+08:00|          100.5|
-|2020-01-01T00:00:10.000+08:00|          119.5|
-|2020-01-01T00:00:12.000+08:00|          101.0|
-|2020-01-01T00:00:14.000+08:00|           99.5|
-|2020-01-01T00:00:16.000+08:00|          119.0|
-|2020-01-01T00:00:18.000+08:00|           80.5|
-|2020-01-01T00:00:20.000+08:00|           99.0|
-|2020-01-01T00:00:22.000+08:00|          121.0|
-|2020-01-01T00:00:24.000+08:00|           79.5|
-+-----------------------------+---------------+
-```
-
-用于查询的SQL语句：
-
-```sql
-select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+--------------------------------------------------+
-|                         Time|seasonalrepair(root.test.d2.s1, 'period'=4, 'k'=2)|
-+-----------------------------+--------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                             100.0|
-|2020-01-01T00:00:04.000+08:00|                                             120.0|
-|2020-01-01T00:00:06.000+08:00|                                              80.0|
-|2020-01-01T00:00:08.000+08:00|                                             100.5|
-|2020-01-01T00:00:10.000+08:00|                                             119.5|
-|2020-01-01T00:00:12.000+08:00|                                              87.0|
-|2020-01-01T00:00:14.000+08:00|                                              99.5|
-|2020-01-01T00:00:16.000+08:00|                                             119.0|
-|2020-01-01T00:00:18.000+08:00|                                              80.5|
-|2020-01-01T00:00:20.000+08:00|                                              99.0|
-|2020-01-01T00:00:22.000+08:00|                                             121.0|
-|2020-01-01T00:00:24.000+08:00|                                              79.5|
-+-----------------------------+--------------------------------------------------+
-```
-
-##### 使用改进的分解方法进行修复
-当`method`取值为'Improved'时，本函数将使用改进的分解方法进行数值修复。
-
-输入序列同上，用于查询的SQL语句如下：
-
-```sql
-select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------------------+
-|                         Time|valuerepair(root.test.d2.s1, 'method'='improved', 'period'=3)|
-+-----------------------------+-------------------------------------------------------------+
-|2020-01-01T00:00:02.000+08:00|                                                        100.0|
-|2020-01-01T00:00:04.000+08:00|                                                        120.0|
-|2020-01-01T00:00:06.000+08:00|                                                         80.0|
-|2020-01-01T00:00:08.000+08:00|                                                        100.5|
-|2020-01-01T00:00:10.000+08:00|                                                        119.5|
-|2020-01-01T00:00:12.000+08:00|                                                         81.5|
-|2020-01-01T00:00:14.000+08:00|                                                         99.5|
-|2020-01-01T00:00:16.000+08:00|                                                        119.0|
-|2020-01-01T00:00:18.000+08:00|                                                         80.5|
-|2020-01-01T00:00:20.000+08:00|                                                         99.0|
-|2020-01-01T00:00:22.000+08:00|                                                        121.0|
-|2020-01-01T00:00:24.000+08:00|                                                         79.5|
-+-----------------------------+-------------------------------------------------------------+
-```
--->
-<!--
-
-    Licensed to the Apache Software Foundation (ASF) under one
-    or more contributor license agreements.  See the NOTICE file
-    distributed with this work for additional information
-    regarding copyright ownership.  The ASF licenses this file
-    to you under the Apache License, Version 2.0 (the
-    "License"); you may not use this file except in compliance
-    with the License.  You may obtain a copy of the License at
-    
-        http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing,
-    software distributed under the License is distributed on an
-    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-    KIND, either express or implied.  See the License for the
-    specific language governing permissions and limitations
-    under the License.
-
--->
-
 ## 8. 序列发现
 
 ### 8.1 ConsecutiveSequences
@@ -5354,127 +4913,3 @@ select ar(s0,"p"="2") from root.test.d0
 |1970-01-01T08:00:00.002+08:00|                    -0.2571|
 +-----------------------------+---------------------------+
 ```
-
-### 9.2 Representation
-
-#### 函数简介
-
-本函数用于时间序列的表示。
-
-**函数名:**  Representation
-
-**输入序列:** 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为INT32，长度为`tb*vb`。序列的时间戳从0开始，仅用于表示顺序。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+
-|                         Time|root.test.d0.s0|
-+-----------------------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -2.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|
-+-----------------------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select representation(s0,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-------------------------------------------------+
-|                         Time|representation(root.test.d0.s0,"tb"="3","vb"="2")|
-+-----------------------------+-------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                1|
-|1970-01-01T08:00:00.002+08:00|                                                1|
-|1970-01-01T08:00:00.003+08:00|                                                0|
-|1970-01-01T08:00:00.004+08:00|                                                0|
-|1970-01-01T08:00:00.005+08:00|                                                1|
-|1970-01-01T08:00:00.006+08:00|                                                1|
-+-----------------------------+-------------------------------------------------+
-```
-
-### 9.3 RM
-
-#### 函数简介
-
-本函数用于基于时间序列表示的匹配度。
-
-**函数名:**  RM
-
-**输入序列:** 仅支持两个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。
-
-**参数:**
-
-- `tb`：时间分块数量。默认为10。
-- `vb`：值分块数量。默认为10。
-
-**输出序列:** 输出单个序列，类型为DOUBLE，长度为`1`。序列的时间戳从0开始，序列仅有一个数据点，其时间戳为0，值为两个时间序列的匹配度。
-
-**提示:** 
-
-- `tb `,`vb`应为正整数。
-
-#### 使用示例
-
-##### 指定时间分块数量、值分块数量
-
-输入序列：
-
-```
-+-----------------------------+---------------+---------------+
-|                         Time|root.test.d0.s0|root.test.d0.s1
-+-----------------------------+---------------+---------------+
-|2020-01-01T00:00:01.000+08:00|           -4.0|           -4.0|
-|2020-01-01T00:00:02.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:03.000+08:00|           -3.0|           -3.0|
-|2020-01-01T00:00:04.000+08:00|           -1.0|           -1.0|
-|2020-01-01T00:00:05.000+08:00|            0.0|            0.0|
-|2020-01-01T00:00:06.000+08:00|            1.0|            1.0|
-|2020-01-01T00:00:07.000+08:00|            2.0|            2.0|
-|2020-01-01T00:00:08.000+08:00|            3.0|            3.0|
-|2020-01-01T00:00:09.000+08:00|            4.0|            4.0|
-+-----------------------------+---------------+---------------+
-```
-
-用于查询的 SQL 语句：
-
-```sql
-select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0
-```
-
-输出序列：
-
-```
-+-----------------------------+-----------------------------------------------------+
-|                         Time|rm(root.test.d0.s0,root.test.d0.s1,"tb"="3","vb"="2")|
-+-----------------------------+-----------------------------------------------------+
-|1970-01-01T08:00:00.001+08:00|                                                 1.00|
-+-----------------------------+-----------------------------------------------------+
-```
-