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Using Generative Art to Convey Past and Future Climate Transitions
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Data Formulator: AI-Powered Concept-Driven Visualization Authoring.

Chenglong Wang, John Thompson, Bongshin Lee

    IEEE Transactions on Visualization and Computer Graphics
    |October 23, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Data transformation is a barrier in visualization authoring. Concept binding, using an AI agent in Data Formulator, simplifies this by separating visualization goals from data processing, enabling easier creation of complex visualizations.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Data transformation is a necessary but complex step for creating visualizations.
    • Existing tools require programming or separate data processing, posing a barrier for many users.
    • High-level visualization intents are often coupled with low-level data manipulation.

    Purpose of the Study:

    • To introduce a new visualization paradigm, concept binding, that separates visualization intent from data transformation.
    • To present Data Formulator, an interactive tool realizing the concept binding paradigm.
    • To evaluate the usability and effectiveness of Data Formulator in simplifying complex data transformations for visualization.

    Main Methods:

    • Developed the concept binding paradigm to decouple high-level visualization goals from data processing.
    • Implemented Data Formulator, an interactive tool that uses an AI agent for automated data transformation.
    • Authors define data concepts via natural language or examples and bind them to visual channels.
    • The AI agent transforms data to surface concepts and generate visualizations, with feedback provided to users.

    Main Results:

    • Data Formulator successfully enabled users to create visualizations requiring challenging data transformations.
    • A user study with 10 participants demonstrated learnability and effective use of the tool.
    • The concept binding approach proved effective in simplifying the visualization authoring process.

    Conclusions:

    • Concept binding, implemented in Data Formulator, significantly lowers the barrier to creating complex data visualizations.
    • Automated data transformation via AI agents can empower users without extensive programming skills.
    • The tool facilitates a more intuitive and efficient visualization authoring workflow.