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    Area of Science:

    • Information Visualization
    • Human-Computer Interaction
    • Data Analysis

    Background:

    • Translating user needs into abstract tasks is challenging for visualization researchers.
    • Existing low-level task classifications lack higher-level analytical context.
    • A gap exists in connecting domain-specific user actions to abstract visualization tasks.

    Purpose of the Study:

    • To propose a novel framework for abstracting visualization analysis goals.
    • To bridge the gap between domain-specific user actions and abstract task classifications.
    • To provide a structured, top-down approach for understanding visualization design and evaluation.

    Main Methods:

    • Open-coding analysis reports from 20 IEEE InfoVis design studies (2009-2015).
    • Developing a two-axis framework with nine distinct analysis goals.
    • Categorizing goals by specificity (Explore, Describe, Explain, Confirm) and data populations (Single, Multiple).

    Main Results:

    • A framework organizing nine analysis goals (e.g., Discover Observation, Compare Entities).
    • Each goal is defined by input, output, and characteristic analysis steps.
    • Demonstrated application of the framework in a top-down abstraction approach.

    Conclusions:

    • The proposed framework facilitates a more intuitive, goal-oriented abstraction of user analysis needs.
    • It aids visualization designers in systematically encoding user tasks within broader analytical contexts.
    • This approach enhances the interpretation and application of existing task classifications in visualization research.