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    Area of Science:

    • Computer Science
    • Data Visualization
    • Scientific Computing

    Background:

    • Overplotting is a significant challenge in visualizing large datasets, obscuring data patterns and outliers.
    • Existing scatterplot sampling techniques struggle with efficiency and preserving data density accurately.

    Purpose of the Study:

    • To develop a pyramid-based scatterplot sampling technique for efficient large data visualization.
    • To enable progressive and streaming visualization capabilities for dynamic data exploration.
    • To preserve relative data densities and outliers effectively during sampling.

    Main Methods:

    • A multiresolution pyramid-based decomposition of the data's density map is employed.
    • Density values within the pyramid guide sampling at different scales.
    • The technique is adapted for chunk-based processing to support progressive and streaming visualization.

    Main Results:

    • The proposed technique achieves competitive quality compared to state-of-the-art methods.
    • It demonstrates an order of magnitude improvement in processing speed.
    • Quantitative evaluation confirms stable and faithful progressive samples, superior in outlier preservation and frame-switching stability.

    Conclusions:

    • The pyramid-based scatterplot sampling technique offers an efficient and effective solution for large data visualization.
    • Its progressive and streaming capabilities enhance dynamic data exploration.
    • The method provides superior preservation of data densities and outliers compared to existing approaches.