Abstract
Materialized views aim to improve the response time of analytical queries posed on a data warehouse. This entails that they contain information that provides answers to most future queries. The selection of such information from the data warehouse is referred to as view selection. View selection deals with selection of appropriate sets of views to improve the query response time. Several view selection algorithms exist in literature, most of them being greedy based. The greedy algorithm HRUA, which selects top-k views from a multidimensional lattice, is considered the most fundamental greedy based algorithm. It selects views having the highest benefit, computed in terms of size, for materialization. Though the views selected using HRUA are beneficial with respect to size, they may not account for a large number of future queries and may hence become an unnecessary overhead. This problem is addressed by the Query Answering Greedy Algorithm (QAGA) proposed in this paper. QAGA uses both the size of the view, and the frequency of previously posed queries answered by each view, to compute the profits of all views in each iteration. Thereafter it selects, from among them, the most profitable view for materialization. QAGA is able to select views which are beneficial with respect to size and have a greater likelihood of answering future queries. Further, experimental results show that QAGA, as compared to HRUA, is able to select views capable of answering greater number of queries. Though HRUA incurs a lower total cost of evaluating all the views, QAGA has a lower total cost of answering all the queries leading to an improvement in the average query response time. This in turn facilitates decision making.