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Clustering-based materialized view selection in data warehouses (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Jouve, Pierre-Emmanuel Aouiche, Kamel |
| Description | In ADBIS’06, volume 4152 of LNCS |
| Abstract | Abstract. The 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 process 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. 1 |
| File Format | |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Candidate View Non-trivial Task Similar Query Data Warehouse Judicious Choice Test Data Warehouse Materialized View Selection Greedy Process Decision-support Query Data Mining Technique Storage Space Experimental Result Clustering-based Materialized View Selection Cost Model |
| Content Type | Text |
| Resource Type | Article |
