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VizSnippets: Compressing Visualization Bundles Into Representative Previews for Browsing Visualization Collections.

Michael Oppermann, Tamara Munzner

    IEEE Transactions on Visualization and Computer Graphics
    |October 1, 2021
    PubMed
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    Current visualization snippets lack sufficient detail, hindering users from assessing content relevance. This study introduces a systematic approach and computational pipeline for improved visualization snippet design, offering richer previews.

    Area of Science:

    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Interactive visualization collections are widely used for sharing analytical knowledge.
    • Existing result snippets (title + one image) are insufficient for users to judge bundle relevance.
    • Users often open visualization bundles to examine details, a time-consuming process.

    Purpose of the Study:

    • To propose the first systematic approach to designing effective visualization snippets.
    • To address key challenges in creating informative and compact previews of visualization bundles.
    • To improve user experience in discovering relevant analytical content.

    Main Methods:

    • Developed a framework for visualization snippet design addressing eight identified challenges.
    • Created a computational pipeline to compress visual and textual bundle content into representative previews.

    Related Experiment Videos

  • Designed adaptive previews considering pixel budget, information density, multiple images, and keywords.

    • Main Results:

    • The proposed snippet design framework offers a structured approach to improving previews.
    • The computational pipeline generates high-information-density previews adaptable to pixel constraints.
    • The method allows for richer, more informative previews than current title-and-image designs.

    Conclusions:

    • Effective visualization snippet design requires a systematic approach addressing specific challenges.
    • Computational methods can generate dense, multi-faceted previews to aid content discovery.
    • This work lays the foundation for more informative and user-friendly visualization browsing experiences.