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    Data compression is a bottleneck in scientific computing. This study introduces a progressive compression framework that adapts accuracy to analysis needs, improving data handling without sacrificing results.

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    Area of Science:

    • Scientific data compression
    • High-performance computing
    • Data analysis and visualization

    Background:

    • Data transfer bottlenecks impede scientific simulations, observations, and experiments.
    • Traditional lossy compression methods require strict error tolerances, limiting adaptability for diverse data analysis tasks.
    • Progressive data compression offers adaptable accuracy but lacks inherent support in many analysis algorithms and frameworks.

    Purpose of the Study:

    • To present a novel framework enabling progressive-precision data queries for diverse data compressors and numerical representations.
    • To address the challenges of adapting existing compression techniques and file formats for progressive data processing.
    • To offer a flexible solution for scientific data analysis where accuracy requirements vary per task.

    Main Methods:

    • Developed a multi-component representation for progressive error reduction.
    • Integrated the framework with four established scientific data compressors.
    • Evaluated effectiveness using real-world datasets from the SDRBench collection.

    Main Results:

    • The framework achieves accuracy comparable to standalone compressors.
    • Compression and decompression times scale proportionally with the number of components requested.
    • Achieved fully lossless compression with lossy compressors by employing a sufficient number of components.

    Conclusions:

    • The proposed framework effectively enables progressive-precision data queries across various compressors.
    • It offers a scalable and accurate solution to data transfer bottlenecks in scientific computing.
    • The approach enhances data analysis flexibility by allowing task-specific accuracy control.