UnchartIt: An Interactive Framework for Program Recovery from Charts
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 program in the top-10 in $92%$ of the instances. To disambiguate those programs, we use our new interactive disambiguation procedure, which successfully returns the correct program on 98% of the ambiguous instances by asking on average fewer than 2 questions to the user.
Tue 22 Sep Times are displayed in time zone: (UTC) Coordinated Universal Time
|09:10 - 09:30|
|Verified from Scratch: Program Analysis for Learners' Programs|
|09:30 - 09:50|
|Interval Change-Point Detection for Runtime Probabilistic Model Checking|
Xingyu ZhaoHeriot-Watt University, Radu CalinescuUniversity of York, UK, Simos GerasimouUniversity of York, UK, Valentin RobuHeriot-Watt University, David FlynnHeriot-Watt UniversityPre-print
|09:50 - 10:10|
|UnchartIt: An Interactive Framework for Program Recovery from Charts|