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A Provenance Task Abstraction Framework.

Christian Bors, John Wenskovitch, Michelle Dowling

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    This study introduces a task abstraction framework to infer complex user activities from varying levels of provenance data in visual analytics tools. The framework enhances analytical capabilities by addressing context, variability, and uncertainty.

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    Area of Science:

    • Computer Science
    • Information Science

    Background:

    • Visual analytics tools utilize provenance recording to track analytic processes and user insights.
    • Provenance data can be captured at different levels of detail, enabling varied characterizations of user activities.

    Purpose of the Study:

    • To propose a novel task abstraction framework for inferring user activities across multiple provenance granularities.
    • To address limitations in current approaches that cannot infer activities requiring multi-level provenance analysis.

    Main Methods:

    • Developed a three-stage task abstraction framework: initializing a provenance task hierarchy, parsing it using an abstraction mapping mechanism, and leveraging it in analytical tools.
    • Identified implications for iterative refinement, context, variability, and uncertainty within the framework.

    Main Results:

    • The proposed framework enables the inference of activities characterized across multiple levels of provenance.
    • A use case demonstrates how context influences the provenance hierarchy to support analysis.

    Conclusions:

    • The task abstraction framework offers a method to enhance visual analytics by inferring complex activities from multi-level provenance.
    • Further research is needed to address implementation challenges related to iterative refinement, context, variability, and uncertainty.