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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Kołaczkowski, P. (2008). Compressing very large database workloads for continuous online index selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5181 LNCS, pp. 791–799). https://doi.org/10.1007/978-3-540-85654-2_71
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