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    Visual analytics (VA) struggles with big data. Customizing computational methods with low-precision and interactive visualization enables real-time big data analysis.

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    Area of Science:

    • Data Science
    • Computer Science
    • Information Visualization

    Background:

    • Exponential data growth increases complexity and obscurity.
    • Visual analytics (VA) is gaining attention but faces scalability challenges with big data.
    • Computational methods offer compact data representation but require significant computation time, hindering real-time interaction.

    Purpose of the Study:

    • To address the limitations of current computational methods in visual analytics for big data.
    • To enable real-time interactive visual analytics for large datasets.
    • To bridge the gap between computational efficiency and visualization interactivity.

    Main Methods:

    • Customizing computational methods for VA by addressing precision and convergence discrepancies.
    • Implementing low-precision computation for faster processing.
    • Utilizing iteration-level interactive visualization for real-time feedback.

    Main Results:

    • Achieved real-time interactive visual analytics capabilities for big data.
    • Demonstrated the effectiveness of customized computational methods in enhancing VA performance.
    • Successfully balanced computational efficiency with interactive visualization.

    Conclusions:

    • Customized computational methods, including low-precision computation and iteration-level visualization, are crucial for real-time big data VA.
    • These approaches overcome the scalability limitations of traditional VA solutions.
    • Enables more effective exploration and analysis of large-scale datasets.