Abstract
Data integration system usually runs on unpredictable and volatile environments. Query cost model should be update with the changes of the environment. In this paper, we tackle this problem by evolving the cost model so that it can adapt to the environment change and keep up-to-date. Firstly, the factors causing the system environment to change are analyzed and different methods are proposed to deal with these changes. Then an architecture for evolving a cost model in dynamic environment is proposed. Our experimental results show the architecture of evolving a cost model in dynamic environment can well capture changes of environment and keep cost models up-to-date.
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© 2005 Springer-Verlag Berlin Heidelberg
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Liao, Z., Wang, H., Glass, D., Guo, G. (2005). KNN Based Evolutionary Techniques for Updating Query Cost Models. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_99
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DOI: https://doi.org/10.1007/11540007_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
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