Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data
Mengyu Zhou, Tao Wang, Pengxin Ji, Shi Han, Zhang Dong-mei
Abstract
Given a table of multi-dimensional data, what analyses would human create to extract information from it? From scientific exploration to business intelligence (BI), this is a key problem to solve towards automation of knowledge discovery and decision making. In this paper, we propose Table2Analysis to learn commonly conducted analysis patterns from large amount of (table, analysis) pairs, and recommend analyses for any given table even not seen before. Multi-dimensional data as input challenges existing model architectures and training techniques to fulfill the task. Based on deep Q-learning with heuristic search, Table2Analysis does table to sequence generation, with each sequence encoding an analysis. Table2Analysis has 0.78 recall at top-5 and 0.65 recall at top-1 in our evaluation against a large scale spreadsheet corpus on the PivotTable recommendation task.