A parallel optimization framework in grid environment

Authors:
Kun Gao;Hongshan Yang;Kexiong Chen;Meiqun Liu;Jiaxun Chen
Affiliations:
Information Science and Technology College, Donghua University, P.R.C and Aviation University of Air Force, P.R.C;Information Science and Technology College, Donghua University, P.R.C;Aviation University of Air Force, P.R.C;Administration of Radio Film and Television of Jilin Province, P.R.C;Information Science and Technology College, Donghua University, P.R.C
Venue:
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
Year:
2005

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Abstract

Grid is a solution to computationally and data intensive computing problems. Since the distributed knowledge discovery process is both data and computational intensive, the Grid is a natural platform for deploying a high performance data mining service. The approach to efficient data mining is parallelization, where the whole computation is broken up into parallel tasks. Existing mechanisms of data mining parallelization are based on NOW or SMP, it is necessary to develop new parallel mechanism for grid feature. We propose a new framework for easily and efficiently parallelizing data mining algorithms on Grid. The framework decomposes tasks according to each of the existing computing power of grid.