An Improved View Selection Algorithm in Data Warehouses by Finding Frequent Queries

A data warehouse is a source for storing historical data to support decision making. Usually analytic queries take much time. To solve response time problem it should be materialized some views to answer all queries in minimum response time. There are many solutions for view selection problems. The most appropriate solution for view selection is materializing frequent queries. Previously posed queries on the data warehouse have profitable information. These queries probably will be used in the future. So, previous queries are clustered using clustering algorithms. Then frequent queries are found using data mining algorithms. Therefore optimal queries are found in each cluster. In the last stage optimal queries are merged to produce one (query) view for each cluster, and materializes this view. This paper proposes an algorithm for materializing frequent queries. The algorithm finds profitable views using previously posed queries on the data warehouse. These views can answer the most of the queries being posed in the future. This paper uses Index-BittableFI algorithm for finding frequent views. Using this algorithm improves previous view selection algorithms and reduces the response time. The experiments show that the proposed algorithm has %23 improvement in response time and %50 improvement in storage space.