UnchartIt

Abstract

Charts are commonly used for data visualization. Generating a chart usually involves performing data transformations, including data pre-processing and aggregation. These tasks can be cumbersome and time-consuming, even for experienced data scientists. Reproducing existing charts can also be a challenging task when information about data transformations is no longer available. In this paper, we tackle the problem of recovering data transformations from existing charts. Given an input table and a chart, our goal is to automatically recover the data transformation program underlying the chart. We divide our approach into four steps: (1) data extraction, (2) candidate generation, (3) candidate ranking, and (4) candidate disambiguation. We implemented our approach in a tool called UNCHARTIT and evaluated it on a set of 50 benchmarks from Kaggle. Experimental results show that UNCHARTIT successfully ranks the correct data transformation among the top-10 programs in 92% of the benchmarks. To disambiguate the top-ranking programs, we use our new interactive procedure, which successfully disambiguates 98% of the ambiguous benchmarks by asking on average fewer than 2 questions to the user.