Name
Abstract | ||
|---|---|---|
Materialized view selection is a non-trivial task. Hence, its complexity must be reduced. A judicious choice of views must be cost-driven and influenced by the workload experienced by the system. In this paper, we propose a framework for materialized view selection that exploits a data mining technique (clustering), in order to determine clusters of similar queries. We also propose a view merging algorithm that builds a set of candidate views, as well as a greedy process for selecting a set of views to materialize. This selection is based on cost models that evaluate the cost of accessing data using views and the cost of storing these views. To validate our strategy, we executed a workload of decision-support queries on a test data warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when storage space is limited. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11827252_9 | ADBIS'06 Proceedings of the 10th East European conference on Advances in Databases and Information Systems |
Keywords | DocType | Volume |
materialized view selection,test data warehouse,accessing data,greedy process,cost model,candidate view,decision-support query,view selection,data mining technique,data mining,data warehouse,decision support,materialized views | Conference | abs/cs/0703114 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-37899-5 | 46 |
PageRank | References | Authors |
1.42 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kamel Aouiche | 1 | 233 | 13.32 |
| Pierre-Emmanuel Jouve | 2 | 66 | 6.07 |
| Jérôme Darmont | 3 | 382 | 50.93 |