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Rough Set Based Data Mining Tasks Scheduling on Knowledge Grid

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Advances in Web Intelligence (AWIC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3528))

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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.

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References

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Author information

Authors and Affiliations

  1. Information Science and Technology College, Donghua University, 1882 West Yan’an Road, Shanghai, P.R.C

    Kun Gao & Jiaxun Chen

  2. Aviation University of Air Force, P.R.C

    Kun Gao & Kexiong Chen

  3. Administration of Radio Film and Television of Jilin Province, P.R.C

    Meiqun Liu

Authors
  1. Kun Gao
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  2. Kexiong Chen
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  3. Meiqun Liu
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  4. Jiaxun Chen
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Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Piotr S. Szczepaniak

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Institute of Computer Science, Technical University of Łódź, Poland

    Adam Niewiadomski

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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)

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