Falx: Synthesis-Powered Visualization Authoring
This addresses the challenge for data analysts who need to manually transform data to match visualization designs, offering a novel automation approach that is not incremental but introduces a new paradigm.
The paper tackles the problem of data layout mismatches in visualization tools, which require significant manual data transformation, by introducing Falx, a synthesis-powered tool that automatically infers specifications and transforms data from user examples, enabling users to create visualizations they otherwise could not implement, as shown in a study with 33 data analysts on four tasks.
Modern visualization tools aim to allow data analysts to easily create exploratory visualizations. When the input data layout conforms to the visualization design, users can easily specify visualizations by mapping data columns to visual channels of the design. However, when there is a mismatch between data layout and the design, users need to spend significant effort on data transformation. We propose Falx, a synthesis-powered visualization tool that allows users to specify visualizations in a similarly simple way but without needing to worry about data layout. 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. In a study with 33 data analysts on four visualization tasks involving data transformation, we found that users can effectively adopt Falx to create visualizations they otherwise cannot implement.