Merge branch 'master' into dev

README.md

@@ -8,38 +8,64 @@
 
 <img src="https://ch-rath.oss-ap-northeast-1.aliyuncs.com/assets/kanaries-light-bg.png" alt="logo" width="180px" style="" />
 
-Augmented Analytics tool. Help you automate data analysis and visualization recommendation.
+Augmented Analytics tool. Help you automate explore data and recommend visualization based on human visual perception.
 
+
+Rath can recommend high-dimensional visualization which contains deep and complex patterns.
+
++ Try Latest version !! [demo(latest)](https://ch-rath.oss-ap-northeast-1.aliyuncs.com/)
++ Try Stable version [demo(stable)](https://kanaries.github.io/Rath/)
 + [Youtube Video Demo](https://www.youtube.com/watch?v=o3_PH1Cbql4)
 + [Bilibili Video Demo](https://www.bilibili.com/video/av82089992/)
 
 ## Introduction
 
-Rath helps you extract insights from datasource automatically and generate interactive visualizations with interesting findings. Rath can auto generate high dimensional visualization contains complex patterns while most other auto-EDA tools only provideing simple low dimensional charts with basic statistics pattern. Its means you can use Rath to explore the data to a deep level and find more insights.
+Rath helps you extract insights from datasource automatically and generate interactive visualizations with interesting findings. Rath can auto generate high dimensional visualization contains complex patterns while most other auto-EDA tools only providing simple low dimensional charts with basic statistics pattern. Its means you can use Rath to explore the data to a deep level and find more insights.
+
+Rath design an algorithm recommending visualization with lowest perception error by human eyes, which means you can read the info in visualization much accurate[1]. The origin idea is mentioned in APT, 1987 by Mackinlay[2]. It suggests using visual channel with lower perception error to encode more important fields. However, 'the importance of field' usually is hard to measure. Works like data voyager (compassQL)[3] suppose the fields chosen earlier by user are more important, which means it cannot be used in an automated process where exists no chosen order.
 
-Rath design a algorithm recommanding visualization with lowest perception error by human eyes, which means you can read the info in visualization much accuracte. 
+Rath compute how much extra view entropy(similar thoughts like entropy, but not exactly the same) a field will bring to a visualization, and use lower perception error visual channel to encode field bring more entropy to the visualization view.
+
+Rath can also automate generate best interactive logic of visualizations in a dashboard. It builds a relation graph of visualization set and choose a sub graph which has max sum of edge score(represent relation). It make the `selection-filter` interaction represents more meaningful correlation or dependency between fields in different visualization.
 
 Here are main parts in Rath,
 
 ### DataSource
 DataSource board is for data uploading, sampling(currently support stream data, which means there is no limit of the size of file you uploaded), cleaning and defining fields type(dimensions, measures). In visual insights, we regard dimensions as independent variable or feature and measures as dependent variable or target.
 
-### Notebook
-Notebook is a board for user to know what happened in the automatic analysis process and how rath uses visual-insights. It shows how decisions are made by the application and provide interactive interface to adjust some of the parameters and operators used by the algorithm. 
+![dataSource-en.png](https://ch-resources.oss-cn-shanghai.aliyuncs.com/kanaries/Rath-Demos/dataSource-en.png)
 
-### Gallery
-Gallery displays parts of the visualization with interesting findings. In Gallery, you can find interesting visualizaiton and use association feature to find more related visualization. You can also search specific info in gallery. There are some settings here to adjust some of the visual elements in the chart.
+### Gallery / Explore
+Rath automate scan your datasource and find interesting deep patterns which will be displayed in Gallery / Explore segment.
+ In Gallery, you can find interesting visualization and use association feature to find more related visualization. You can also search specific info in gallery. There are some settings here to adjust some of the visual elements in the chart.
 
-### Explainer
-Explainer uses serveral insight discoverary algorithm to detect what is the specific insight type is shown in a visualization recommanded. Explainer is an extension of B. Tang 's Top K insight paper.
+![gallery.png](https://ch-resources.oss-cn-shanghai.aliyuncs.com/kanaries/Rath-Demos/explore-en.png)
+
+### Graphic Walker
+Graphic Walker is a lite tableau style visual analysis interface. It is used for cases when users have specific analytic target or user want to analysis further result based on the recommended results by Rath's auto insights.
+![graphic-walker.png](https://ch-resources.oss-cn-shanghai.aliyuncs.com/kanaries/Rath-Demos/graphic-walker-en.png)
+
+Graphic Walker is an independent embedding module. You can use it independently in your own app. See more details in `packages/graphic-walker/README.md`
+
+```bash
+yarn add @kanaries/graphic-walker
+# or
+
+npm i --save @kanaries/graphic-walker
+```
 
 ### Dashboard
-Generate interactive dashboards for your. Rath will figure out sets of visulizations of which contents are connected to each other and can be used to analysis a specific problem. 
+Generate interactive dashboards for your. Rath will figure out sets of visualizations of which contents are connected to each other and can be used to analysis a specific problem.
+![](https://ch-resources.oss-cn-shanghai.aliyuncs.com/kanaries/Rath-Demos/dashboard-en.png)
+
+### Notebook
+Notebook is a board for user to know what happened in the automatic analysis process and how rath uses visual-insights. It shows how decisions are made by the application and provide interactive interface to adjust some of the parameters and operators used by the algorithm.
+
+### Explainer
+Explainer uses several insight discovery algorithm to detect what is the specific insight type is shown in a visualization recommended. Explainer is an extension of B. Tang 's Top K insight paper[4].
 
-### Graphic Walker
-Graphic Walker is a lite tableau style visual analysis interface. It is used for cases when users have specific analytic target or user want to analysis further result based on the recommanded results by Rath's auto insights.
 
-You can also use Graphic Walker as a lite tableau style analysis app independently. It can be used as an independent app or an embeding module.
+You can also use Graphic Walker as a lite tableau style analysis app independently. It can be used as an independent app or an embedding module.
 
 more details can be found in README.md in graphic-walker folder.
 
@@ -54,8 +80,8 @@ Details of the test result can be accessed [here](https://www.yuque.com/chenhao-
 ## Usage
 
 ### Try online demo
-+ on Github Pages [demo](https://kanaries.github.io/Rath/)
-+ on Alibaba Cloud OSS [demo](https://ch-rath.oss-ap-northeast-1.aliyuncs.com/)
++ on Github Pages(Stable version) [demo](https://kanaries.github.io/Rath/)
++ on Alibaba Cloud OSS(Latest version) [demo](https://ch-rath.oss-ap-northeast-1.aliyuncs.com/)
 
 ### Download Desktop Version
 - [MacOS](https://ch-resources.oss-cn-shanghai.aliyuncs.com/downloads/rath/Kanaries%20Rath-0.1.0.dmg)
@@ -67,6 +93,7 @@ Rath now runs all the computation tasks on webworker. If you are interested in a
 
 (dev)
 ```bash
+yarn install
 # under project root dir
 yarn workspace graphic-walker build
 
@@ -77,6 +104,8 @@ yarn workspace frontend start
 
 production mode
 ```bash
+yarn install
+
 yarn workspace graphic-walker build
 
 yarn workspace frontend build
@@ -86,7 +115,7 @@ yarn workspace frontend build
 
 only use the algorithm package. (`/packages/visual-insights`) ![](https://img.shields.io/npm/v/visual-insights?color=blue)
 ```bash
-npm i visual-insights --save`
+npm i visual-insights --save
 ```
 
 ## Documentation
@@ -97,9 +126,8 @@ npm i visual-insights --save`
 ## Reference
 
 Rath is insipired by several excellent works below:
-
-+ Wongsuphasawat, Kanit, et al. "Voyager 2: Augmenting visual analysis with partial view specifications." Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 2017.
-+ B. Tang et al, "Extracting top-K insights from multi-dimensional data," in 2017, . DOI: 10.1145/3035918.3035922.
-+ Vega-Lite: A Grammar of Interactive Graphics. Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, Jeffrey Heer. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2017
-+ Cleveland, W., & McGill, R. (1984). Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association, 79(387), 531-554. doi:10.2307/2288400
-
++ [1] Cleveland, W., & McGill, R. (1984). Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association, 79(387), 531-554. doi:10.2307/2288400
++ [2] Mackinlay, Jock D. Automatic design of graphical presentations. Stanford Univ., CA (USA), 1987.
++ [3] Wongsuphasawat, Kanit, et al. "Voyager 2: Augmenting visual analysis with partial view specifications." Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 2017.
++ [4] B. Tang et al, "Extracting top-K insights from multi-dimensional data," in 2017, . DOI: 10.1145/3035918.3035922.
++ [5] Vega-Lite: A Grammar of Interactive Graphics. Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, Jeffrey Heer. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2017

README.zh-CN.md

@@ -9,7 +9,8 @@
 <img src="https://ch-rath.oss-ap-northeast-1.aliyuncs.com/assets/kanaries-light-bg.png" alt="logo" width="360px" style="" />
 
 Rath是新一代的基于增强分析技术的可视化分析工具，它提供了自动化的数据探索分析能力与自动可视化设计能力。Rath既可以在你对数据无从下手时提供分析入口的建议，也可以在你的分析过程中提供实时的分析辅助和建议。Rath会帮你完成大部分数据探索分析的工作，使得你可以专注于领域问题本身。当数据越复杂时，Rath的自动化能力带来的优势就越加显著。
-
++ 体验最新版 !! [demo(latest)](https://ch-rath.oss-ap-northeast-1.aliyuncs.com/)
++ 体验稳定版 [demo(stable)](https://kanaries.github.io/Rath/)
 + [Youtube 视频 Demo](https://www.youtube.com/watch?v=o3_PH1Cbql4)
 + [Bilibili 视频 Demo](https://www.bilibili.com/video/av82089992/)
 
@@ -28,13 +29,39 @@ Rath 可以根据人眼的视觉感知的准确度进行可视化设计，这使
 
 作者极力推荐的必看 Rath使用教程：[《Tutorial: 使用Rath快速获取数据洞察》](https://www.yuque.com/docs/share/3f32e044-3530-4ebe-9b01-287bfbdb7ce0?#)
 
-### Graphic Walker
+### 数据集上传
+在数据集界面导入你的数据集，这里会生成一个大致的预览。在这里，你可以完善一些对数据集信息的补充，也可以直接使用机器默认识别的配置。通常你需要关注一下哪些字段是度量，哪些是维度，以及是否存在一些脏数据，你打算如何处理他们。
+
+![](https://ch-resources.oss-cn-shanghai.aliyuncs.com/kanaries/Rath-Demos/dataSource-cn.png)
+
+### 探索分析页面
+Rath会自动化的探索你的数据集，并推荐给你数据集中比较有趣的规律和深层的洞察。在这里，你可以快速浏览Rath推荐的结果，如果你对一些结果感兴趣，可以点击“联想”，来获取和当前视图相关的一些其他视图。如果联想的结果中你也感兴趣，可以把你感兴趣的图表设为主图表，继而继续联想，来完成一个探索的过程。
+
+![](https://ch-resources.oss-cn-shanghai.aliyuncs.com/kanaries/Rath-Demos/gallery-cn.png)
+
+当你对一些推荐结果有明确的思路，想要使用类似tableau的方式进行自定义分析时，可以点击“自定义分析”把结果带到自助分析模块。这里提供了一个tableau风格的分析模块，可以使你使用一些自定义分析的能力。
+
+![](https://ch-resources.oss-cn-shanghai.aliyuncs.com/kanaries/Rath-Demos/graphic-walker-cn.png)
+
+### 一键报表生成
+除了自己探索外，你也可以一键生成数据报表。这些报表会自动生成内部的联动交互的逻辑，你可以对报表进行交互，来进一步探索数据。
+
+![](https://ch-resources.oss-cn-shanghai.aliyuncs.com/kanaries/Rath-Demos/dashboard-en.png)
+
+
+### Graphic Walker（自助分析）
 Rath中包含一个tableau风格的自助分析工具，它是一个和基于图形语法构建的自助分析面板，可以支持非常灵活自由的可视化配置。这是为了帮助用户在有明确分析目的时使用或在Rath推荐的结果上进行进一步的自助分析。
 
 这个模块从工程上是独立的，你可以把它单独作为一个分析应用来使用或者作为一个嵌入式的模块（Rath便是这也使用它）。
 
+```bash
+npm i --save @kanaries/graphic-walker
+```
+
 详细的使用方式详见graphic-walker文件夹下的README.md文件
 
+![](https://ch-resources.oss-cn-shanghai.aliyuncs.com/kanaries/Rath-Demos/editor-en.png)
+
 
 ## 案例
 
@@ -49,8 +76,8 @@ Rath中包含一个tableau风格的自助分析工具，它是一个和基于图
 
 ### 线上demo
 + 官网 [kanaries.net](https://kanaries.net/)
-+ Github Pages [demo](https://kanaries.github.io/Rath/)
-+ 阿里云OSS [demo](https://ch-rath.oss-ap-northeast-1.aliyuncs.com/)
++ Github Pages(稳定版) [demo](https://kanaries.github.io/Rath/)
++ 阿里云OSS(最新版) [demo](https://ch-rath.oss-ap-northeast-1.aliyuncs.com/)
 
 ### 桌面版下载
 - [Mac版](https://ch-resources.oss-cn-shanghai.aliyuncs.com/downloads/rath/Kanaries%20Rath-0.1.0.dmg)