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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Gao, K., Chen, K., Liu, M., Chen, J. (2005). Rough Set Based Data Mining Tasks Scheduling on Knowledge Grid. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds) Advances in Web Intelligence. AWIC 2005. Lecture Notes in Computer Science(), vol 3528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11495772_24
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DOI: https://doi.org/10.1007/11495772_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26219-0
Online ISBN: 978-3-540-31900-9
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
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