Clustering-Based Materialized View Selection in Data Warehouses

materialized view selection is a non-trivial task. Hence, its complexity must be reduced. A judicious choice of views must be costdriven 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 decisionsupport queries on a test data warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when storage space is limited.

• Type: Conference
• Year: 2006
• Where: ADBIS
• Authors: Kamel Aouiche, Pierre-Emmanuel Jouve, Jérôme Darmont
• Keywords: ADBIS 2006, Data Mining Technique, Database, Materialized View Selection, View Merging Algorithm