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12

ADMA
2005
Springer

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Heuristic Scheduling of Concurrent Data Mining Queries

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Heuristic Scheduling of Concurrent Data Mining Queries

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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 our heuristic algorithm called CCFull, which suboptimally schedules the data mining queries into a number of separate execution phases. The algorithm significantly outperforms the optimal approach while providing a very good accuracy.

Marek Wojciechowski, Maciej Zakrzewicz

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ADMA 2005 | Data Mining | Data Mining Queries | Execution Cost | Separate Execution Phases |

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Added	26 Jun 2010
Updated	26 Jun 2010
Type	Conference
Year	2005
Where	ADMA
Authors	Marek Wojciechowski, Maciej Zakrzewicz

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