Abstract
Greedy view selection, in each iteration, selects the most beneficial view for materialization. Algorithm HRUA, the most fundamental greedy based algorithm, uses the size of the views to select the top-k beneficial views from a multidimensional lattice. HRUA does not take into account the query frequency of each view and as a consequence it may select views which may not be beneficial in respect of answering future queries. As a result, the selected views may not contain relevant and required information for answering queries leading to an unnecessary space overhead. This problem is addressed by the algorithm proposed in this paper, which considers both the size and the query frequency of each view to select the top-k views. The views so selected are profitable with respect to size and are capable of answering large number of queries. Further, experiments show that the views selected using the proposed algorithm, in comparison to those selected using HRUA, are able to answer comparatively greater number of queries at the cost of a slight drop in the total cost of evaluating all the views. This in turn aids in reducing the query response time and facilitates decision making.