Materialized Views Selection for Answering Queries

A data warehouse stores historical data to support analytical query processing. These analytical queries are long and complex and processing these against a large data warehouse consumes a lot of time. As a result, the query response time is high. One way to reduce this time is by selecting views that are likely to answer a large number of future queries and storing them in a data warehouse. This problem is referred to as view selection. Several view selection algorithms have been proposed with most of these being focused around HRUA. HRUA considers the size of the views to select the most beneficial view for materialization. The views selected using HRUA, though beneficial with respect to size, may be unable to account for large numbers of queries and thus making them an unnecessary overhead. The algorithm proposed in this paper attempts to address this problem by considering query frequency, along with the size, of the view to select Top-K views for materialization. The proposed algorithm, in each iteration, computes the profit, defined in terms of size and query frequency, and then selects the most profitable view for materialization. As a result, the views selected are beneficial with respect to size and have the ability to answer future queries. Further, experimental results show that the proposed algorithm, in comparison to HRUA, is able to select views capable of answering larger number of queries against a slight increase in the total cost of evaluating all the views. This in turn would result in efficient decision making.