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    This study introduces a general model for guidance in visual analytics (VA), extending existing visualization models. It aims to systematically develop techniques that assist users in analyzing complex data more effectively.

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    Area of Science:

    • Computer Science
    • Information Visualization

    Background:

    • Visual analytics (VA) tools often mirror data complexity, hindering user analysis.
    • Existing guidance methods lack a unifying framework for effective user assistance.

    Purpose of the Study:

    • To establish a general model for reasoning about guidance in visual analysis.
    • To systematically develop and improve user-centric guidance techniques for VA.

    Main Methods:

    • Extended van Wijk's visualization model to incorporate fundamental guidance components.
    • Defined guidance as a process narrowing the gap in data analysis continuation.
    • Analyzed diverse inputs for guidance generation and discussed degrees of guidance.

    Main Results:

    • Presented a novel model for understanding and implementing guidance in VA.
    • Illustrated the model using existing literature-based guidance approaches.
    • Identified key research challenges and future directions for VA guidance.

    Conclusions:

    • The proposed model facilitates systematic development of effective VA guidance techniques.
    • This work paves the way for improved user support in complex data analysis.
    • Future research should focus on refining and applying the guidance model in practice.