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Related Experiment Video

Updated: May 27, 2026

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

An Integrated Framework for Parameter-based Optimization of Scientific Workflows.

Vijay S Kumar, P Sadayappan, Gaurang Mehta

    Proceedings of the ... International Symposium on High Performance Distributed Computing
    |November 10, 2011
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a framework for optimizing scientific data analysis workflows. It balances performance gains with potential impacts on output accuracy, demonstrated through spatial data analysis applications.

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    Area of Science:

    • Computer Science
    • Geospatial Analysis

    Background:

    • Scientific data analysis often involves complex, coarse-grain workflows with interdependencies.
    • Optimizing these workflows requires navigating a multi-dimensional parameter space.

    Purpose of the Study:

    • To present an integrated framework for performance optimization of scientific workflows.
    • To support multi-dimensional parameter optimization, considering trade-offs between performance and accuracy.

    Main Methods:

    • Developed an integrated framework for workflow performance optimization.
    • Evaluated the framework using two real-world spatial data analysis applications.

    Main Results:

    • Demonstrated the framework's capability to support performance optimizations across multiple dimensions.
    • Showcased experimental results from spatial data analysis applications.

    Conclusions:

    • The proposed framework effectively supports multi-dimensional performance optimization for scientific workflows.
    • The framework allows for strategic trade-offs between workflow performance and output accuracy.