Heuristic Scheduling of Concurrent Data Mining Queries

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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.