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    This study introduces an automated framework for adapting multiple-view (MV) visualizations to various screen sizes. The new method enhances user preference and usability for data visualization on diverse devices.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Multiple-view (MV) visualizations are widely used for data exploration and communication.
    • Current MV visualizations are primarily designed for desktop use, limiting their effectiveness on diverse screen sizes.
    • Adapting complex visualizations to different displays remains a significant challenge.

    Purpose of the Study:

    • To present a novel two-stage framework for automated adaptation of desktop MV visualizations to varying display sizes.
    • To enable both automated layout preservation and semi-automated visual tailoring for responsive data visualization.
    • To evaluate the effectiveness and user preference of the adapted visualizations compared to existing methods.

    Main Methods:

    • Developed a two-stage adaptation framework for MV visualizations.
    • Employed simulated annealing to address layout retargeting as an optimization problem, preserving view arrangements.
    • Utilized a rule-based auto-configuration method and an interactive interface for fine-tuning visual encoding and appearance.

    Main Results:

    • Successfully adapted desktop MV visualizations for rendering on small displays.
    • User study results indicated a preference for visualizations generated by the proposed approach.
    • Participants found the adapted visualizations to be more user-friendly and easier to use.

    Conclusions:

    • The proposed framework effectively supports automated retargeting and semi-automated tailoring of MV visualizations for diverse screen sizes.
    • The approach enhances the usability and user preference of data visualizations across different devices.
    • This work contributes a feasible and expressive solution for responsive data visualization design.