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Clusters and Grids for Distributed and Parallel Knowledge Discovery

  • Mario Cannataro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1823)

Abstract

Parallel and Distributed Knowledge Discovery (PDKD) is emerging as a possible killer application for clusters and grids of computers. The need to process large volumes of data and the availability of parallel data mining algorithms, makes it possible to exploit the increasing computational power of clusters at low costs. On the other side, grid computing is an emerging “standard” to develop and deploy distributed, high performance applications over geographic networks, in different domains, and in particular for data intensive applications. This paper proposes an approach to integrate cluster of computers within a grid infrastructure to use them, enriched by specific data mining services, as the deployment platform for high performance distributed data mining and knowledge discovery.

Keywords

Data Mining Service Level Agreement Cluster Computing Grid Service Data Mining Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Mario Cannataro
    • 1
  1. 1.ISI-CNRRendeItaly

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