Query Frequency based View Selection

View selection deals with the selection of appropriate sets of views capable of improving the response times for queries while conforming to space constraints. Materializing all views is infeasible, as the number of possible views is exponential with respect to the number of dimensions and, thus, would not fit within the available storage space. Further, optimal view selection is an NP-Complete problem. Thus, the only remaining alternative is to select a subset of views that reduce the query response time and fit within the available space for materialization. The most fundamental greedy view selection algorithm HRUA considers the size parameter for computing the Top-K views for materialization. In each iteration, it computes the benefit, with respect to size, of all non-selected views, and selects the one entailing the highest benefit for materialization. Though these selected views may be beneficial in respect of their size, they may not be capable of answering large numbers of future queries thereby becoming an unnecessary space overhead. Existing query frequency based view selection algorithms, which address this problem, have been compared in this paper. Experimental results show that each of these algorithms, in comparison to HRUA, are able to select fairly good quality views that provide answers to comparatively greater numbers of queries. Materializing these selected views would facilitate the business decision making process.