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Mosaic Selections: Managing and Optimizing User Selections for Scalable Data Visualization Systems.

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    Summary
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    Mosaic Selections optimize interactive visualizations for large datasets. This model enables rapid, low-latency data filtering and updates across multiple visualizations, improving performance for millions of records.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Interactive visualizations struggle with real-time analysis of large datasets (millions+ records).
    • User selections for filtering data can be complex and require low-latency updates.

    Purpose of the Study:

    • To introduce Mosaic Selections, a novel model for managing and optimizing user selections in interactive visualizations.
    • To enable efficient, real-time interaction with large datasets.

    Main Methods:

    • Developed Mosaic Selections, a model integrating filter predicates into data queries for visualizations and input widgets.
    • Implemented automatic optimizations, including pre-aggregating data, based on query and selection predicate analysis.
    • Formalized the selection model and optimization techniques within the open-source Mosaic architecture.

    Main Results:

    • Achieved orders-of-magnitude latency improvements for selection-based optimizations compared to unoptimized queries and existing Vega optimizers.
    • Demonstrated efficient handling of complex, multi-component user selections.
    • Validated scalability to millions and billions of records.

    Conclusions:

    • Mosaic Selections provide a flexible and interoperable framework for data filtering across visualizations.
    • The model's automatic optimizations significantly enhance the performance of interactive visualizations with large datasets.
    • Enables real-time interaction and analysis for massive data scales.