Heuristic Scheduling of Concurrent Data Mining Queries

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.

• Type: Conference
• Year: 2005
• Where: ADMA
• Authors: Marek Wojciechowski, Maciej Zakrzewicz
• Keywords: ADMA 2005, Data Mining, Data Mining Queries, Execution Cost, Separate Execution Phases