Abstract
Execution cost of batched data mining queries can be reduced by integrating their I/O steps. Due to memory limitations, not all data mining queries in a batch can be executed together. In this paper we introduce a heuristic algorithm called CCFull,which suboptimally schedules the data mining queries into a number of execution phases. The algorithm significantly outperforms the optimal approach while providing a very good accuracy.
This work was partially supported by the grant no. 4T11C01923 from the State Committee for Scientific Research (KBN), Poland.
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Wojciechowski, M., Zakrzewicz, M. (2005). Heuristic Scheduling of Concurrent Data Mining Queries. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_38
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DOI: https://doi.org/10.1007/11527503_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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