2008 | OriginalPaper | Chapter
Compressing Very Large Database Workloads for Continuous Online Index Selection
Author : Piotr Kołaczkowski
Published in: Database and Expert Systems Applications
Publisher: Springer Berlin Heidelberg
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The paper presents a novel method for compressing large database workloads for purpose of autonomic, continuous index selection. The compressed workload contains a small subset of representative queries from the original workload. A single pass clustering algorithm with a simple and elegant selectivity based query distance metric guarantees low memory and time complexity. Experiments on two real-world database workloads show the method achieves high compression ratio without decreasing the quality of the index selection problem solutions.