Abstract

When data mining and knowledge discovery techniques must be used to analyze large amounts of data, high-performance parallel and distributed computers can help to provide better computational performance and, as a consequence, deeper and more meaningful results. Recently grids, composed of large-scale, geographically distributed platforms working together, have emerged as effective architectures for high-performance decentralized computation. It is natural to consider grids as tools for distributed data-intensive applications such as data mining, but the underlying patterns of computation and data movement in such applications are different from those of more conventional high-performance computation. These differences require a different kind of grid, or at least a grid with significantly different emphases. This paper discusses the main issues, requirements, and design approaches for the implementation of grid-based knowledge discovery systems. Furthermore, some prospects and promising research directions in datacentric and knowledge-discovery oriented grids are outlined.

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Cite this article

@article{cai-1245,
  author  = {David Skillicorn and Domenico Talia},
  title   = {Mining Large Data Sets on Grids: Issues and Prospects},
  journal = {Computing and Informatics},
  volume  = {21},
  number  = {4},
  pages   = {347--362},
  year    = {2002},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2002/4/mining-large-data-sets-on-grids-issues-and-prospects/}
}
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