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Provectories: Embedding-Based Analysis of Interaction Provenance Data.

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    Summary
    This summary is machine-generated.

    This study introduces a new visual method to analyze user behavior and analytical strategies from interaction data. The approach effectively extracts user patterns and reasoning strategies from complex interaction histories.

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    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Cognitive Science

    Background:

    • Understanding user behavior and analytical strategies is challenging using traditional manual methods.
    • Capturing user interaction data is possible, but extracting analysis strategies from this provenance is difficult.

    Purpose of the Study:

    • To propose a novel visual approach for the meta-analysis of user interaction provenance.
    • To develop methods for extracting user behavior patterns and analytical reasoning strategies from interaction data.

    Main Methods:

    • Representing single and multiple user sessions as graphs of high-dimensional application states.
    • Utilizing two-dimensional embeddings (topology-based and attribute similarity-based) for meta-analysis.
    • Applying visualization techniques to synthetic and real user provenance data.

    Main Results:

    • Successfully visualized complex user interaction data.
    • Extracted discernible patterns related to data types and analytical reasoning strategies.
    • Demonstrated the effectiveness of the visual approach in analyzing user provenance.

    Conclusions:

    • The proposed visual meta-analysis approach offers a powerful method for understanding user behavior and analytical strategies.
    • This technique overcomes limitations of manual analysis by providing insights from interaction provenance.
    • The findings have implications for designing more effective user interfaces and supporting analytical tasks.