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Related Experiment Video

Updated: May 22, 2026

Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices
07:40

Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices

Published on: March 10, 2023

Parameterized Specification, Configuration and Execution of Data-Intensive Scientific Workflows.

Vijay S Kumar1, Tahsin Kurc, Varun Ratnakar

  • 1Dept. of Computer Science and Engineering, Ohio State University, Columbus, OH 43210.

Cluster Computing
|May 25, 2012
PubMed
Summary

This study introduces a framework for optimizing scientific data analysis workflows by tuning performance parameters. It enables balancing workflow speed with output accuracy for complex data processing tasks.

Related Experiment Videos

Last Updated: May 22, 2026

Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices
07:40

Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices

Published on: March 10, 2023

Area of Science:

  • Computational Science
  • Data Science
  • Scientific Workflow Management

Background:

  • Scientific data analysis often involves complex, coarse-grain workflows with interdependencies.
  • Optimizing these workflows requires navigating a multidimensional parameter space affecting execution time and accuracy.

Purpose of the Study:

  • To present an integrated framework supporting performance optimizations across multiple parameters in scientific workflows.
  • To evaluate the framework's effectiveness using real-world applications in multidimensional data analysis.

Main Methods:

  • Representing data analysis processes as coarse-grain workflows with data flow dependencies.
  • Viewing performance optimization as a search in a parameter space affecting execution times.
  • Developing a framework to manage trade-offs between performance and output quality.

Main Results:

  • The proposed framework supports integrated performance optimizations.
  • Experimental evaluation on two real-world spatial, multidimensional data analysis applications demonstrates the framework's utility.

Conclusions:

  • The integrated framework effectively supports performance optimization of scientific workflows.
  • The approach allows for balancing execution speed and output accuracy in complex data analysis.