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Posted to commits@iotdb.apache.org by su...@apache.org on 2022/01/19 06:35:51 UTC
[iotdb] branch master updated: Website 0.13 (#4896)
This is an automated email from the ASF dual-hosted git repository.
sunzesong pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/iotdb.git
The following commit(s) were added to refs/heads/master by this push:
new 9e1d3bc Website 0.13 (#4896)
9e1d3bc is described below
commit 9e1d3bc4ebcfef69358da424bfe8f16e66a3ba3d
Author: Zesong Sun <sz...@mails.tsinghua.edu.cn>
AuthorDate: Wed Jan 19 14:34:04 2022 +0800
Website 0.13 (#4896)
Co-authored-by: Zesong Sun <v-...@microsoft.com>
---
Jenkinsfile | 2 +-
docs/UserGuide/Process-Data/UDF-Library.md | 64 +++++++++++++-------------
docs/zh/UserGuide/Process-Data/UDF-Library.md | 66 +++++++++++++--------------
site/pom.xml | 64 ++++++++++++++++++++++++++
4 files changed, 130 insertions(+), 66 deletions(-)
diff --git a/Jenkinsfile b/Jenkinsfile
index 0f5e6b6..1943bd9 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -70,7 +70,7 @@ pipeline {
}
steps {
// Publish the site with the scm-publish plugin.
- sh 'mvn -P site -P compile-site -P compile-site-0.12 -P compile-site-0.11 -P compile-site-0.10 -P compile-site-0.9 -P compile-site-0.8 compile scm-publish:publish-scm -pl site'
+ sh 'mvn -P site -P compile-site -P compile-site-0.13 -P compile-site-0.12 -P compile-site-0.11 -P compile-site-0.10 -P compile-site-0.9 -P compile-site-0.8 compile scm-publish:publish-scm -pl site'
// Clean up the snapshots directory (freeing up more space after deploying).
dir("target") {
diff --git a/docs/UserGuide/Process-Data/UDF-Library.md b/docs/UserGuide/Process-Data/UDF-Library.md
index 44c6ff3..d06355f 100644
--- a/docs/UserGuide/Process-Data/UDF-Library.md
+++ b/docs/UserGuide/Process-Data/UDF-Library.md
@@ -24,18 +24,18 @@ For applications based on time series data, data quality is vital.
**UDF Library** is IoTDB User Defined Functions (UDF) about data quality, including data profiling, data quality evalution and data repairing.
It effectively meets the demand for data quality in the industrial field.
-## Quick Start
+### Quick Start
1. Download the JAR with all dependencies and the script of registering UDF.
2. Copy the JAR package to `ext\udf` under the directory of IoTDB system.
3. Run `sbin\start-server.bat` (for Windows) or `sbin\start-server.sh` (for Linux or MacOS) to start IoTDB server.
4. Copy the script to the directory of IoTDB system (under the root directory, at the same level as `sbin`), modify the parameters in the script if needed and run it to register UDF.
-## Contact
+### Contact
+ Email: iotdb-quality@protonmail.com
-## Download
+### Download
You can download the following files:
@@ -58,11 +58,11 @@ You can download the following files:
</tr>
</table>
-# Data Quality
+## Data Quality
-## Completeness
+### Completeness
-### Usage
+#### Usage
This function is used to calculate the completeness of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the completeness of each window will be output.
**Name:** COMPLETENESS
@@ -78,7 +78,7 @@ This function is used to calculate the completeness of time series. The input se
**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output.
-### Example: Default Parameters
+#### Example: Default Parameters
With default parameters, this function will regard all input data as the same window.
@@ -122,7 +122,7 @@ Output series:
+-----------------------------+-----------------------------+
```
-### Example: Specific Window Size
+#### Example: Specific Window Size
When the window size is given, this function will divide the input data as multiple windows.
@@ -182,9 +182,9 @@ Output series:
+-----------------------------+--------------------------------------------+
```
-## Consistency
+### Consistency
-### Usage
+#### Usage
This function is used to calculate the consistency of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the consistency of each window will be output.
**Name:** CONSISTENCY
@@ -199,7 +199,7 @@ This function is used to calculate the consistency of time series. The input ser
**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output.
-### Example: Default Parameters
+#### Example: Default Parameters
With default parameters, this function will regard all input data as the same window.
@@ -243,7 +243,7 @@ Output series:
+-----------------------------+----------------------------+
```
-### Example: Specific Window Size
+#### Example: Specific Window Size
When the window size is given, this function will divide the input data as multiple windows.
@@ -303,9 +303,9 @@ Output series:
+-----------------------------+-------------------------------------------+
```
-## Timeliness
+### Timeliness
-### Usage
+#### Usage
This function is used to calculate the timeliness of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the timeliness of each window will be output.
**Name:** TIMELINESS
@@ -320,7 +320,7 @@ This function is used to calculate the timeliness of time series. The input seri
**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output.
-### Example: Default Parameters
+#### Example: Default Parameters
With default parameters, this function will regard all input data as the same window.
@@ -364,7 +364,7 @@ Output series:
+-----------------------------+---------------------------+
```
-### Example: Specific Window Size
+#### Example: Specific Window Size
When the window size is given, this function will divide the input data as multiple windows.
@@ -424,9 +424,9 @@ Output series:
+-----------------------------+------------------------------------------+
```
-## Validity
+### Validity
-### Usage
+#### Usage
This function is used to calculate the Validity of time series. The input series are divided into several continuous and non overlapping windows. The timestamp of the first data point and the Validity of each window will be output.
**Name:** VALIDITY
@@ -441,7 +441,7 @@ This function is used to calculate the Validity of time series. The input series
**Note:** Only when the number of data points in the window exceeds 10, the calculation will be performed. Otherwise, the window will be ignored and nothing will be output.
-### Example: Default Parameters
+#### Example: Default Parameters
With default parameters, this function will regard all input data as the same window.
@@ -485,7 +485,7 @@ Output series:
+-----------------------------+-------------------------+
```
-### Example: Specific Window Size
+#### Example: Specific Window Size
When the window size is given, this function will divide the input data as multiple windows.
@@ -545,9 +545,9 @@ Output series:
+-----------------------------+----------------------------------------+
```
-# Data Repair
+## Data Repair
-## TimestampRepair
+### TimestampRepair
This function is used for timestamp repair.
According to the given standard time interval,
@@ -568,7 +568,7 @@ this function will use the **median**, **mode** or **cluster** of the time inter
**Output Series:** Output a single series. The type is the same as the input. This series is the input after repairing.
-### Example: Manually Specify the Standard Time Interval
+#### Example: Manually Specify the Standard Time Interval
When `interval` is given, this function repairs according to the given standard time interval.
@@ -617,7 +617,7 @@ Output series:
+-----------------------------+----------------------------------------------------+
```
-### Example: Automatically Estimate the Standard Time Interval
+#### Example: Automatically Estimate the Standard Time Interval
When `interval` is default, this function estimates the standard time interval.
@@ -646,9 +646,9 @@ Output series:
+-----------------------------+--------------------------------+
```
-## ValueFill
+### ValueFill
-### Usage
+#### Usage
This function is used to impute time series. Several methods are supported.
**Name**: ValueFill
@@ -663,7 +663,7 @@ This function is used to impute time series. Several methods are supported.
**Note:** AR method use AR(1) model. Input value should be auto-correlated, or the function would output a single point (0, 0.0).
-### Example: Fill with linear
+#### Example: Fill with linear
When `method` is "linear" or the default, Screen method is used to impute.
@@ -721,7 +721,7 @@ Output series:
+-----------------------------+-----------------------+
```
-### Example: Previous Fill
+#### Example: Previous Fill
When `method` is "previous", previous method is used.
@@ -755,9 +755,9 @@ Output series:
+-----------------------------+-------------------------------------------+
```
-## ValueRepair
+### ValueRepair
-### Usage
+#### Usage
This function is used to repair the value of the time series.
Currently, two methods are supported:
**Screen** is a method based on speed threshold, which makes all speeds meet the threshold requirements under the premise of minimum changes;
@@ -780,7 +780,7 @@ Currently, two methods are supported:
**Note:** `NaN` will be filled with linear interpolation before repairing.
-### Example: Repair with Screen
+#### Example: Repair with Screen
When `method` is 'Screen' or the default, Screen method is used.
@@ -838,7 +838,7 @@ Output series:
+-----------------------------+----------------------------+
```
-### Example: Repair with LsGreedy
+#### Example: Repair with LsGreedy
When `method` is 'LsGreedy', LsGreedy method is used.
Input series is the same as above, the SQL for query is shown below:
diff --git a/docs/zh/UserGuide/Process-Data/UDF-Library.md b/docs/zh/UserGuide/Process-Data/UDF-Library.md
index 149e38b..89a7fca 100644
--- a/docs/zh/UserGuide/Process-Data/UDF-Library.md
+++ b/docs/zh/UserGuide/Process-Data/UDF-Library.md
@@ -20,21 +20,21 @@
-->
# UDF 函数库
-## 什么是 UDF 函数库
+### 什么是 UDF 函数库
对基于时序数据的应用而言，数据质量至关重要。**UDF 函数库** 基于 IoTDB 用户自定义函数 (UDF)，实现了一系列关于数据质量的函数，包括数据画像、数据质量评估与修复等，有效满足了工业领域对数据质量的需求。
-## 快速开始
+### 快速开始
1. 下载包含全部依赖的 jar 包和注册脚本；
2. 将 jar 包复制到 IoTDB 程序目录的`ext\udf`目录下；
3. 运行`sbin\start-server.bat`（在 Windows 下）或`sbin\start-server.sh`（在 Linux 或 MacOS 下）以启动 IoTDB 服务器；
4. 将注册脚本复制到 IoTDB 的程序目录下（与`sbin`目录同级的根目录下），修改脚本中的参数（如果需要）并运行注册脚本以注册 UDF。
-## 联系我们
+### 联系我们
+ Email: iotdb-quality@protonmail.com
-## 下载
+### 下载
您可以下载下列文件：
@@ -57,11 +57,11 @@
</tr>
</table>
-# 数据质量
+## 数据质量
-## 完整性(Completeness)
+### 完整性(Completeness)
-### 函数简介
+#### 函数简介
本函数用于计算时间序列的完整性。将输入序列划分为若干个连续且不重叠的窗口，分别计算每一个窗口的完整性，并输出窗口第一个数据点的时间戳和窗口的完整性。
**函数名：** COMPLETENESS
@@ -77,7 +77,7 @@
**提示：** 只有当窗口内的数据点数目超过10时，才会进行完整性计算。否则，该窗口将被忽略，不做任何输出。
-### 示例：参数缺省
+#### 示例：参数缺省
在参数缺省的情况下，本函数将会把全部输入数据都作为同一个窗口计算完整性。
@@ -120,7 +120,7 @@ select completeness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
+-----------------------------+-----------------------------+
```
-### 示例：指定窗口大小
+#### 示例：指定窗口大小
在指定窗口大小的情况下，本函数会把输入数据划分为若干个窗口计算完整性。
@@ -179,9 +179,9 @@ select completeness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01
+-----------------------------+--------------------------------------------+
```
-## 一致性(Consistency)
+### 一致性(Consistency)
-### 函数简介
+#### 函数简介
本函数用于计算时间序列的一致性。将输入序列划分为若干个连续且不重叠的窗口，分别计算每一个窗口的一致性，并输出窗口第一个数据点的时间戳和窗口的时效性。
**函数名：** CONSISTENCY
@@ -196,7 +196,7 @@ select completeness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01
**提示：** 只有当窗口内的数据点数目超过10时，才会进行一致性计算。否则，该窗口将被忽略，不做任何输出。
-### 示例：参数缺省
+#### 示例：参数缺省
在参数缺省的情况下，本函数将会把全部输入数据都作为同一个窗口计算一致性。
@@ -239,7 +239,7 @@ select consistency(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
+-----------------------------+----------------------------+
```
-### 示例：指定窗口大小
+#### 示例：指定窗口大小
在指定窗口大小的情况下，本函数会把输入数据划分为若干个窗口计算一致性。
@@ -298,9 +298,9 @@ select consistency(s1,"window"="15") from root.test.d1 where time <= 2020-01-01
+-----------------------------+-------------------------------------------+
```
-## 时效性(Timeliness)
+### 时效性(Timeliness)
-### 函数简介
+#### 函数简介
本函数用于计算时间序列的时效性。将输入序列划分为若干个连续且不重叠的窗口，分别计算每一个窗口的时效性，并输出窗口第一个数据点的时间戳和窗口的时效性。
**函数名：** TIMELINESS
@@ -315,7 +315,7 @@ select consistency(s1,"window"="15") from root.test.d1 where time <= 2020-01-01
**提示：** 只有当窗口内的数据点数目超过10时，才会进行时效性计算。否则，该窗口将被忽略，不做任何输出。
-### 示例：参数缺省
+#### 示例：参数缺省
在参数缺省的情况下，本函数将会把全部输入数据都作为同一个窗口计算时效性。
@@ -358,7 +358,7 @@ select timeliness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
+-----------------------------+---------------------------+
```
-### 示例：指定窗口大小
+#### 示例：指定窗口大小
在指定窗口大小的情况下，本函数会把输入数据划分为若干个窗口计算时效性。
@@ -417,9 +417,9 @@ select timeliness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 0
+-----------------------------+------------------------------------------+
```
-## Validity
+### Validity
-### 函数简介
+#### 函数简介
本函数用于计算时间序列的有效性。将输入序列划分为若干个连续且不重叠的窗口，分别计算每一个窗口的有效性，并输出窗口第一个数据点的时间戳和窗口的有效性。
@@ -435,7 +435,7 @@ select timeliness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 0
**提示：** 只有当窗口内的数据点数目超过10时，才会进行有效性计算。否则，该窗口将被忽略，不做任何输出。
-### 示例：参数缺省
+#### 示例：参数缺省
在参数缺省的情况下，本函数将会把全部输入数据都作为同一个窗口计算有效性。
@@ -478,7 +478,7 @@ select validity(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
+-----------------------------+-------------------------+
```
-### 示例：指定窗口大小
+#### 示例：指定窗口大小
在指定窗口大小的情况下，本函数会把输入数据划分为若干个窗口计算有效性。
@@ -539,10 +539,10 @@ select validity(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:
-# 数据修复
-## 时间戳修复(TimestampRepair)
+## 数据修复
+### 时间戳修复(TimestampRepair)
-### 函数简介
+#### 函数简介
本函数用于时间戳修复。根据给定的标准时间间隔，采用最小化修复代价的方法，通过对数据时间戳的微调，将原本时间戳间隔不稳定的数据修复为严格等间隔的数据。在未给定标准时间间隔的情况下，本函数将使用时间间隔的中位数 (median)、众数 (mode) 或聚类中心 (cluster) 来推算标准时间间隔。
@@ -558,7 +558,7 @@ select validity(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:
**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。
-### 示例：指定标准时间间隔
+#### 示例：指定标准时间间隔
在给定`interval`参数的情况下，本函数将按照指定的标准时间间隔进行修复。
@@ -606,7 +606,7 @@ select timestamprepair(s1,'interval'='10000') from root.test.d2
+-----------------------------+----------------------------------------------------+
```
-### 示例：自动推算标准时间间隔
+#### 示例：自动推算标准时间间隔
如果`interval`参数没有给定，本函数将按照推算的标准时间间隔进行修复。
@@ -635,9 +635,9 @@ select timestamprepair(s1) from root.test.d2
+-----------------------------+--------------------------------+
```
-## ValueFill
+### ValueFill
-### 函数简介
+#### 函数简介
**函数名：** ValueFill
@@ -710,7 +710,7 @@ select valuefill(s1) from root.test.d2
+-----------------------------+-----------------------+
```
-### 示例：使用 previous 方法进行填补
+#### 示例：使用 previous 方法进行填补
当`method`取值为 'previous' 时，本函数将使前值填补方法进行数值填补。
@@ -744,9 +744,9 @@ select valuefill(s1,"method"="previous") from root.test.d2
+-----------------------------+-------------------------------------------+
```
-## 时间序列数值修复(ValueRepair)
+### 时间序列数值修复(ValueRepair)
-### 函数简介
+#### 函数简介
本函数用于对时间序列的数值进行修复。目前，本函数支持两种修复方法：**Screen** 是一种基于速度阈值的方法，在最小改动的前提下使得所有的速度符合阈值要求；**LsGreedy** 是一种基于速度变化似然的方法，将速度变化建模为高斯分布，并采用贪心算法极大化似然函数。
**函数名：** VALUEREPAIR
@@ -765,7 +765,7 @@ select valuefill(s1,"method"="previous") from root.test.d2
**提示：** 输入序列中的`NaN`在修复之前会先进行线性插值填补。
-### 示例：使用 Screen 方法进行修复
+#### 示例：使用 Screen 方法进行修复
当`method`缺省或取值为 'Screen' 时，本函数将使用 Screen 方法进行数值修复。
输入序列：
@@ -822,7 +822,7 @@ select valuerepair(s1) from root.test.d2
+-----------------------------+----------------------------+
```
-### 示例：使用 LsGreedy 方法进行修复
+#### 示例：使用 LsGreedy 方法进行修复
当`method`取值为 'LsGreedy' 时，本函数将使用 LsGreedy 方法进行数值修复。
输入序列同上，用于查询的 SQL 语句如下：
diff --git a/site/pom.xml b/site/pom.xml
index 14f7482..6bf6046 100644
--- a/site/pom.xml
+++ b/site/pom.xml
@@ -442,6 +442,70 @@
</build>
</profile>
<profile>
+ <id>compile-site-0.13</id>
+ <build>
+ <plugins>
+ <!-- download 0.13 docs from iotdb repo -->
+ <plugin>
+ <groupId>com.googlecode.maven-download-plugin</groupId>
+ <artifactId>download-maven-plugin</artifactId>
+ <version>1.6.7</version>
+ <executions>
+ <execution>
+ <id>get-0.13-docs</id>
+ <phase>generate-sources</phase>
+ <goals>
+ <goal>wget</goal>
+ </goals>
+ <configuration>
+ <url>https://github.com/apache/iotdb/archive/rel/0.13.zip</url>
+ <skipCache>true</skipCache>
+ <overwrite>true</overwrite>
+ <outputDirectory>${project.build.directory}/download</outputDirectory>
+ <outputFileName>0.13.x.zip</outputFileName>
+ </configuration>
+ </execution>
+ </executions>
+ </plugin>
+ <!-- unpack 0.13 docs from the zip files-->
+ <plugin>
+ <groupId>org.codehaus.mojo</groupId>
+ <artifactId>truezip-maven-plugin</artifactId>
+ <version>1.2</version>
+ <inherited>false</inherited>
+ <executions>
+ <execution>
+ <id>copy-0.13-documentation</id>
+ <goals>
+ <goal>copy</goal>
+ </goals>
+ <phase>generate-sources</phase>
+ <configuration>
+ <fileset>
+ <directory>${project.build.directory}/download/0.13.x.zip/iotdb-rel-0.13/docs/UserGuide</directory>
+ <outputDirectory>${project.build.directory}/vue-source/src/UserGuide/V0.13.x</outputDirectory>
+ </fileset>
+ </configuration>
+ </execution>
+ <execution>
+ <id>copy-0.13-zh-documentation</id>
+ <goals>
+ <goal>copy</goal>
+ </goals>
+ <phase>generate-sources</phase>
+ <configuration>
+ <fileset>
+ <directory>${project.build.directory}/download/0.13.x.zip/iotdb-rel-0.13/docs/zh/UserGuide</directory>
+ <outputDirectory>${project.build.directory}/vue-source/src/zh/UserGuide/V0.13.x</outputDirectory>
+ </fileset>
+ </configuration>
+ </execution>
+ </executions>
+ </plugin>
+ </plugins>
+ </build>
+ </profile>
+ <profile>
<id>compile-site-0.12</id>
<build>
<plugins>