TY - GEN
T1 - Query size estimation using clustering techniques
AU - Su, Xiaoyuan
AU - Kubat, Miroslav
AU - Tapia, Moiez A.
AU - Hu, Chao
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33845867667&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845867667&partnerID=8YFLogxK
U2 - 10.1109/ICTAI.2005.105
DO - 10.1109/ICTAI.2005.105
M3 - Conference contribution
AN - SCOPUS:33845867667
SN - 0769524885
SN - 9780769524887
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 185
EP - 189
BT - ICTAI 2005
T2 - ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05
Y2 - 14 November 2005 through 16 November 2005
ER -