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KnowledgePearls: Provenance-Based Visualization Retrieval.

Holger Stitz, Samuel Gratzl, Harald Piringer

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    Summary
    This summary is machine-generated.

    KnowledgePearls efficiently retrieves past analysis states using provenance graphs and visual similarity searches. This system aids users in recalling previous results and guiding future data analyses without manual annotation.

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    Area of Science:

    • Data Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Analytical provenance tracking creates valuable knowledge bases for recalling past results.
    • Manual annotation for meta-information is time-consuming, hindering efficient search and retrieval of analysis states.

    Purpose of the Study:

    • To present KnowledgePearls, a system for efficient retrieval of analysis states.
    • To enable users to query and explore analysis states based on visual similarity to a partial definition.

    Main Methods:

    • Developed KnowledgePearls, structuring analysis states as provenance graphs with automatically recorded user interactions and visualizations.
    • Implemented a visual interface for querying and exploring analysis states based on similarity.
    • Demonstrated approach with demographic analyses (Hans Rosling) and a biomedical case study, independent of visualization frameworks like Vega.

    Main Results:

    • KnowledgePearls facilitates efficient retrieval of past analysis states.
    • The system supports both explicit user queries and inferred definitions from reference states.
    • Successful application shown in demographic and biomedical data exploration.

    Conclusions:

    • KnowledgePearls enhances data analysis exploration by enabling rapid recall of previous states.
    • The approach is versatile, applicable to various visualization frameworks, including Vega-based implementations like Gapminder.
    • Automated provenance tracking and visual similarity search reduce the burden of manual annotation for analysis state retrieval.