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    This study introduces a new knowledge generation model for visual analytics, unifying existing frameworks. The model aids in developing and evaluating visual data analysis systems, fostering better communication among researchers.

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    Area of Science:

    • Computer Science
    • Information Science
    • Human-Computer Interaction

    Background:

    • Visual analytics facilitates analysis of large datasets for decision-making and data exploration.
    • Human cognition is central to knowledge discovery from visual data.
    • Existing frameworks for visual analytics are often narrowly focused and lack a unified perspective.

    Purpose of the Study:

    • To propose a comprehensive knowledge generation model for visual analytics.
    • To integrate diverse existing frameworks into a cohesive model.
    • To provide a guideline for developing and evaluating visual analytics systems.

    Main Methods:

    • Development of a novel knowledge generation model for visual analytics.
    • Comparison of a real-world visual analytics system against the proposed model.
    • Analysis of the model's utility in system development, evaluation, and comparison.

    Main Results:

    • The proposed model effectively guides the development and evaluation of visual analytics systems.
    • The model facilitates comparison between different data analysis systems.
    • It establishes a common language for describing visual analytic processes, enhancing researcher communication.

    Conclusions:

    • The knowledge generation model offers a valuable framework for visual analytics research and practice.
    • The model can be used to identify future research directions in the field.
    • It promotes a standardized approach to understanding and discussing visual analytic processes.