Abstract
Data visualization supports data scientists’ efforts to explore and understand data throughout the analysis process. However, authoring visualization often requires non-trivial effort in data transformation, which significantly raises the visualization authoring barrier.
In this talk, I will show how we reduce visualization authoring effort for end users with Falx, a visualization-by-example tool. In Falx, users specify visualizations using examples of how concrete values in the input are mapped to visual channels, and Falx automatically infers the visualization specification and transforms the data to match the design. Falx's magic comes from (1) its novel interaction model evolved from the grammar of graphics that allows users to express complex tasks with less efforts, and (2) a scalable synthesis algorithm powered by abstract program reasoning techniques that addresses the combinatorial program search challenge. The end result: Falx can efficiently solve 75% of 83 practical visualization tasks collected from online forums and tutorials within 10 seconds (up from 46% that prior algorithms can solve); 33 users in our study can efficiently interact with Falx, and confidently solve challenging exploratory data analysis tasks they cannot easily solve otherwise.