Implementation of a Data Mining System Based on Grid Computing

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Abstract:

Grid computing is a new and quickly developmental calculation model with the development of Internet technology, focuses on integrating distributed, heterogeneous and idle computers from the Internet to be a service system with high performance. This paper gives a brief introduction of grid computing, and do research on the architecture and implementation of a data mining system based on grid computing, that is DMSGrid, a grid computing data mining applications, not only considers efficient parallel computing as a crucial aspect, but also takes into account dynamic resource configuration and provides an engine to execute the algorithm flow specified in an application.

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Periodical:

Advanced Materials Research (Volumes 433-440)

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3230-3234

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Online since:

January 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Foster I, Kesselman C. The grid: Blueprint for a new computing infrastructure[M]. San Francisco: Morgan-Kaufman, (1998).

Google Scholar

[2] Zheng Y E, Ma H, Zhang L. A temporal logic based grid workflow model and scheduling scheme[C]/Proceedings of the 6th International Conference on Grid and Cooperative Computing, 2007: 338-345.

DOI: 10.1109/gcc.2007.28

Google Scholar

[3] Zhang L, Ma H, Jiang Y, et al. Gmpi: A grid based mpi framework and its implementation[J]. Journal of Huazhong University of Science and Technology: Nature Science, 2007, 35(2): 16-19.

Google Scholar

[4] Du N, Wu B, Wang B. A parallel algorithm for enumerating all maximal cliques in complex network[C]/Proceedings of the 6th International Conference on Data Mining Workshop, 2006: 320-324.

DOI: 10.1109/icdmw.2006.17

Google Scholar

[5] Chen P, Wang Y, Wu B, et al. Betweenness research in telecom society network[J]. Journal of Dynamics of Continuous, Discrete and Impulsive Systems(DCDIS), 2006(6).

Google Scholar

[6] Han J, Kamber M. Data mining: Concepts and techniques[M]. San Francisco: Morgan-Kaufman, 2000. 190.

Google Scholar