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    Area of Science:

    • Data Science
    • Scientific Visualization
    • Data Management

    Background:

    • Existing data reduction methods like quantization (precision reduction) and subsampling (resolution reduction) have limitations.
    • Both methods face fundamental limits where further reduction renders data unusable.

    Purpose of the Study:

    • To explore the synergistic benefits of combining precision and resolution reduction strategies.
    • To develop a unified framework for analyzing the trade-offs in data reduction.

    Main Methods:

    • Representing data reduction schemes as progressive bitstreams.
    • Analyzing the impact of different bit orderings (e.g., by resolution, by precision) on approximation error.
    • Computing task-optimized bitstreams to establish lower bounds on error.

    Main Results:

    • Demonstrated that combining precision and resolution reduction can yield greater efficiency than using either method alone.
    • Identified optimal bit orderings for various datasets and analysis tasks.
    • Established task-specific lower bounds for achievable approximation error.

    Conclusions:

    • A combined approach to data reduction offers superior performance over individual methods.
    • The proposed framework provides a principled way to manage the precision-resolution trade-off.
    • Guidance for scientific data management systems to optimize data storage and streaming for efficiency.