Abstract
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.
The work has been granted by Polish Ministry of Education (grant No 3T11C 002 29).
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Kołaczkowski, P. (2008). Compressing Very Large Database Workloads for Continuous Online Index Selection. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2008. Lecture Notes in Computer Science, vol 5181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85654-2_71
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DOI: https://doi.org/10.1007/978-3-540-85654-2_71
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