Rough set based data mining tasks scheduling on knowledge grid

Authors:
Kun Gao;Kexiong Chen;Meiqun Liu;Jiaxun Chen
Affiliations:
Information Science and Technology College, Donghua University, Shanghai, 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, Shanghai, P.R.C
Venue:
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Year:
2005

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Abstract

An important aspect of scheduling data mining applications on Grid is the ability to accurately determine estimation of task completion time. In this paper, we present a holistic approach to estimation that uses rough sets theory to determine a similarity template and then compute a runtime estimate using identified similar task. The approach is based on frequencies of attributes appeared in discernibility matrix. Experimental result validates our hypothesis that rough sets provide an intuitively sound solution to the problem of scheduling tasks in Grid environment.