自动化数据探索分析和智能可视化设计应用. Automatic insights discovery and visualization for data analysis.
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Automatic insights extraction and visualization specification based on
Rath helps you extract insights from datasource automatically and generate interactive visualization with interesting findings.
Here are main parts in Rath,
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 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.
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.
automantic generate dashboard for you. rath will figure out a set of visulization of which contents are connected to each other and can be used to analysis a specific problem.
Details of the test result can be accessed here
yarn workspace visual-insights build
yarn workspace frontend start
yarn workspace backend dev
production mode ```bash yarn workspace visual-insights build
yarn workspace frontend build
yarn workspace backend dev
only use the algorithm package. (
bash npm i visual-insights --save`
The working process are visualized in notebook board in the application. *** Main process of the algorithm is shown in the
notebookboard. *** Here shows how rath use visual-insights to make a analytic pipeline.
For the first step, rath analyze all the fields in the dataset independently. It gets the fields' distributions and calculate its entropy. Besides, it will define a semantic type (
nominal) for each field. More details of the field will be displayed when hover your mouse on the fields.
Then, it will find the fields with high entropy and try to reduce it by grouping the field (for example). Only dimensions participates this process.
In this step, visual insights search the combination of fields. Visual-Insights suppose that any two fields appears in a view should be correlated with each other otherwise they should be display in seperated view. Visual-Insight now use crammver'V and pearson' cc for different types of fields' correlation.
for example, the correlation of measures:
It helps you to cluster all the measures based on their correlation. It puts all the variables who are strongly related together to make a specific view (with specified dimenions).
After we get many subspaces, we can check the insight significance of each space. Currently, visual-insights support trend, outlier, group(whether different groups of data behave differently for spefic measures)
Rath is insipired by several excellent works below:
Rath is original from Mome Raths in Alice's Adventures in Wonderland.
The word Rath is used in SAO Alicization as name of org. developing Soul Translator (STL) and Under World, which creates A.L.I.C.E.