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DataWink: Reusing and Adapting SVG-Based Visualization Examples with Large Multimodal Models.

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    Summary
    This summary is machine-generated.

    DataWink empowers users to create custom data visualizations by adapting high-quality examples. This system uses large multimodal models (LMMs) to simplify visualization creation for non-experts.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Creating aesthetically pleasing data visualizations is difficult for users lacking design expertise or tool familiarity.
    • Existing visualization tools often have steep learning curves.

    Purpose of the Study:

    • To introduce DataWink, a novel system for creating custom data visualizations by adapting existing high-quality examples.
    • To enable users with limited design or technical skills to generate personalized visualizations.

    Main Methods:

    • Utilized large multimodal models (LMMs) to extract data encoding from SVG visualization examples.
    • Developed an intermediate representation bridging SVG primitives and visualization programs.
    • Integrated a conversational agent for user goal expression and on-demand widget generation for appearance control.
    • Implemented an interactive interface for modifying data mappings and visual design elements.

    Main Results:

    • DataWink was evaluated through a user study (N=12) involving replication and free-form exploration tasks.
    • Participants recognized DataWink for its learnability and effectiveness in personalized authoring.
    • The system successfully maintained aesthetic quality during user modifications.

    Conclusions:

    • Example-driven approaches show significant potential for democratizing data visualization creation.
    • DataWink offers a viable solution for users seeking to create custom, aesthetically pleasing visualizations without extensive expertise.