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A Survey on Annotations in Information Visualization: Empirical Studies, Applications and Challenges.

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    This summary is machine-generated.

    This survey analyzes 191 papers on annotations in information visualization. It categorizes annotation types, methods, and applications to improve future visualization design and research.

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    Area of Science:

    • Information Visualization
    • Human-Computer Interaction
    • Data Science

    Background:

    • Annotations are crucial in information visualization for guiding user attention, clarifying patterns, and aiding interpretation.
    • Existing research on annotations is fragmented across various visualization contexts and applications.

    Purpose of the Study:

    • To conduct a comprehensive survey of empirical studies, tools, techniques, and systems involving annotations in information visualization.
    • To characterize annotations based on their types, generation methods, and targets.
    • To examine the application of annotations in user engagement, storytelling, collaboration, and exploratory data analysis.

    Main Methods:

    • Systematic literature review of 191 research papers.
    • Structured analysis of annotation characteristics (types, generation, targets).
    • Categorization of annotation use across four primary application domains.

    Main Results:

    • Identified diverse annotation types, generation methods, and target elements within visualization systems.
    • Documented the prevalent use of annotations in user engagement, storytelling, collaboration, and exploratory data analysis.
    • Highlighted key trends, practical challenges, and open research questions in the field.

    Conclusions:

    • The findings provide a foundational understanding for designing more effective annotation systems.
    • This work advances future research by identifying critical areas and directions for annotation in visualization.
    • An interactive web resource is available for further exploration of the surveyed papers.