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. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Gao, K., Chen, K., Liu, M., & Chen, J. (2005). Rough set based data mining tasks scheduling on knowledge grid. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3528 LNAI, pp. 150–155). Springer Verlag. https://doi.org/10.1007/11495772_24
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