Parallel and Grid-Based Data Mining – Algorithms, Models and Systems for High-Performance KDD

  • Antonio Congiusta
  • Domenico Talia
  • Paolo Trunfio


Data Mining often is a computing intensive and time requiring process. For this reason, several Data Mining systems have been implemented on parallel computing platforms to achieve high performance in the analysis of large data sets. Moreover, when large data repositories are coupled with geographical distribution of data, users and systems, more sophisticated technologies are needed to implement high-performance distributed KDD systems. Since computational Grids emerged as privileged platforms for distributed computing, a growing number of Grid-based KDD systems has been proposed. In this chapter we first discuss different ways to exploit parallelism in the main Data Mining techniques and algorithms, then we discuss Grid-based KDD systems. Finally, we introduce the Knowledge Grid, an environment which makes use of standard Grid middleware to support the development of parallel and distributed knowledge discovery applications.

Key words

Parallel Data Mining Grid-based Data Mining Knowledge Grid Distributed Knowledge Discovery 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Antonio Congiusta
    • 1
  • Domenico Talia
    • 1
  • Paolo Trunfio
    • 1
  1. 1.DEIS – University of CalabriaCosenzaItaly

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