Falx: Synthesis-Powered Visualization Authoring
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Feb 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 2; 2025: 1.
8 papers.
- 2023Data Formulator: AI-powered Concept-driven Visualization Authoringcited by 31
- 2024Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Waycited by 19
- 2022Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQLcited by 12
- 2021EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendationcited by 8
- 2023Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytellingcited by 7
- 2023Mystique: Deconstructing SVG Charts for Layout Reusecited by 4
- 2025Interactive Program Synthesis for Modeling Collaborative Physical Activities from Narrated Demonstrationscited by 2
- 2024Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualizationcited by 0
- 2025Interactive Program Synthesis for Modeling Collaborative Physical Activities from Narrated Demonstrations29 Sep 2025
- 2024Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way28 Aug 2024
- 2024Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization8 Jul 2024
- 2023Data Formulator: AI-powered Concept-driven Visualization Authoring18 Sep 2023
- 2023Mystique: Deconstructing SVG Charts for Layout Reuse25 Jul 2023
- 2023Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling17 Apr 2023
- 2022Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL25 May 2022
- 2021EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation15 Dec 2021