Query size estimation using clustering techniques

Xiaoyuan Su, Miroslav Kubat, Moiez A. Tapia, Chao Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

For managing the performance of database management systems, we need to be able to estimate the size of queries. Query Size Estimation (QSE) is difficult if the queries are associated with more than one attribute. Here, we propose, and experimentally evaluate, a novel technique that builds on cluster analysis. Empirical results indicate that, in particular, density-based clustering QSE techniques are beneficial for medium and large sized databases where they compare favourably with partitioning clustering QSE ones such as k-means. This is observed especially in the case of noisy and dense datasets.

Original languageEnglish (US)
Title of host publicationICTAI 2005
Subtitle of host publication17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05
Pages185-189
Number of pages5
DOIs
StatePublished - Dec 1 2005
EventICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05 - Hong Kong, China
Duration: Nov 14 2005Nov 16 2005

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2005
ISSN (Print)1082-3409

Other

OtherICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05
CountryChina
CityHong Kong
Period11/14/0511/16/05

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Su, X., Kubat, M., Tapia, M. A., & Hu, C. (2005). Query size estimation using clustering techniques. In ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05 (pp. 185-189). [1562934] (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2005). https://doi.org/10.1109/ICTAI.2005.105