Query Size Estimation Using Clustering Techniques
- Xiaoyuan Su
- Miroslav Kubat, School of Information Technology and Engineering, University of Ottawa
- Moiez A. Tapia
- Chao Hu
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.
Citation
X. Su, M. Kubat, M. Tapia, C. Hu. "Query Size Estimation Using Clustering Techniques". Fifteenth IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp 185-189, November 2005.| Keywords: | Query Size Estimation, Clustering Techniques, Density-based Clustering |
| Category: | In Conference |
BibTeX
@incollection{Su+al:ICTAI05,
author = {Xiaoyuan Su and Miroslav Kubat and Moiez A. Tapia and Chao Hu},
title = {Query Size Estimation Using Clustering Techniques},
Pages = {185-189},
booktitle = {Fifteenth IEEE International Conference on Tools with Artificial
Intelligence (ICTAI)},
year = 2005,
}Submitted by Xiaoyuan Su
