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Distributed data mining on grids: services, tools, and applications.

Mario Cannataro1, Antonio Congiusta, Andrea Pugliese

  • 1Università di Catanzaro, 88100 Catanzaro, Italy. cannataro@unicz.it

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 29, 2004
PubMed
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This study introduces Knowledge Grid, a framework for distributed knowledge discovery. It enables efficient data mining on large, distributed datasets using grid computing resources.

Area of Science:

  • Computer Science
  • Data Science
  • Distributed Computing

Background:

  • Large datasets in science and industry require distributed and parallel processing.
  • Grid computing offers computational support for distributed knowledge discovery.

Purpose of the Study:

  • To present the Knowledge Grid framework for developing data mining applications on grids.
  • To describe the toolset for implementing distributed knowledge discovery.

Main Methods:

  • Designing and implementing data mining applications using Knowledge Grid tools.
  • Searching grid resources, composing software and data components.
  • Executing data mining processes on a grid infrastructure.

Main Results:

Related Experiment Videos

  • The Knowledge Grid framework facilitates the development of distributed data mining applications.
  • The provided toolset supports the entire process from resource discovery to execution.
  • Performance results demonstrate the system's effectiveness.

Conclusions:

  • Knowledge Grid provides a robust framework for distributed knowledge discovery.
  • The system enhances the efficiency of analyzing large, distributed datasets.
  • Grid computing is a valuable resource for advanced data mining tasks.