MARKOV CHAIN FOR THE RECOMMENDATION OF MATERIALIZED VIEWS IN REAL-TIME DATA WAREHOUSE

In this paper we propose an approach, which is based on Markov Chain, to cluster and recommend candidate views for the selection algorithm of materialized views. Our idea is to intervene at regular period of time in order to filter the candidate views which will be used by an algorithm for the selection of materialized views in real-time data warehouse. The aim is to reduce the complexity and the execution cost of the online selection of materialized views. Our experiment results have shown that our solution is very efficient to specify the more profitable views and to improve the query response time.