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 language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI |
| Pages | 185-189 |
| Number of pages | 5 |
| Volume | 2005 |
| DOIs | |
| State | Published - Dec 1 2005 |
| Event | ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05 - Hong Kong, China Duration: Nov 14 2005 → Nov 16 2005 |
Other
| Other | ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 11/14/05 → 11/16/05 |
Fingerprint
ASJC Scopus subject areas
- Engineering(all)
Cite this
Query size estimation using clustering techniques. / Su, Xiaoyuan; Kubat, Miroslav; Tapia, Moiez A.; Hu, Chao.
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. Vol. 2005 2005. p. 185-189 1562934.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Query size estimation using clustering techniques
AU - Su, Xiaoyuan
AU - Kubat, Miroslav
AU - Tapia, Moiez A.
AU - Hu, Chao
PY - 2005/12/1
Y1 - 2005/12/1
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
VL - 2005
SP - 185
EP - 189
BT - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ER -