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A Data Model and Task Space for Data of Interest (DOI) Eye-Tracking Analyses.

Radu Jianu, Sayeed Safayet Alam

    IEEE Transactions on Visualization and Computer Graphics
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    This study introduces gaze to object mapping (GTOM), or data-of-interest (DOI) analysis, to track what users view in visualizations. This method offers new research possibilities beyond traditional eye-tracking fixation points.

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    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Cognitive Science

    Background:

    • Traditional eye-tracking analysis focuses on fixation points or predefined areas of interest (AOI).
    • There is a growing need to understand *what* users are looking at, not just *where*.

    Purpose of the Study:

    • To establish a foundation for data-of-interest (DOI) analysis in eye-tracking research.
    • To introduce and define gaze to object mapping (GTOM) as a novel data collection method.

    Main Methods:

    • Instrumenting visualization code to map gaze coordinates directly to data objects.
    • Developing a DOI data model and comparing it to the AOI data model.
    • Defining and exemplifying DOI-enabled tasks and experiments across different domains.

    Main Results:

    • Demonstrated the reliability and low overhead of GTOM/DOI data collection.
    • Highlighted the structural and scale differences between DOI and AOI data.
    • Presented three concrete examples of DOI experimentation in diverse fields.

    Conclusions:

    • DOI analysis enables novel research workflows previously not possible with traditional methods.
    • Immediate challenges include developing a robust framework for visual support in DOI experimentation and analysis.