Revealing the Semantics of Data Wrangling Scripts With COMANTICS

Xiong, Kai; Luo, Zhongsu; Fu, Siwei; Wang, Yongheng; Xu, Mingliang; Wu, Yingcai

doi:10.1109/tvcg.2022.3209470

Cited by 5 publications

(1 citation statement)

References 45 publications

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“…Because code generation techniques generalize programs from incomplete user specifications, generated programs are inherently ambiguous, and thus require disambiguation to identify a correct solution among candidates. Prior work proposes techniques to visualize the search process [61], visualize code candidates [52,59], and present distinguishing examples for authors to inspect [15]. Data Formulator provides feedback to the authors by presenting the generated code together with its execution results for them to inspect, select, and edit.…”

Section: Related Workmentioning

confidence: 99%

Data Formulator: AI-powered Concept-driven Visualization Authoring

Wang,

Thompson,

Lee

2023

IEEE Trans. Visual. Comput. Graphics

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With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions.

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