Abstract
View selection concerns selection of appropriate set of views for materialization subject to constraints like size, space, time etc. However, selecting optimal set of views for a higher dimensional data set is an NP-Hard problem. Alternatively, views can be selected by exploring the search space in a greedy manner. Several greedy algorithms for view selection exist in literature among which HRUA is considered the most fundamental. HRUA exhibits high run time complexity primarily because the number of possible views that it needs to evaluate is exponential in the number of dimensions. As a result, it would become infeasible to select views for higher dimensional data sets. The Proposed Views Greedy Algorithm (PVGA), presented in this paper, addresses this problem by selecting views from a smaller set of proposed views, instead of all the views in the lattice as in case of HRUA. This would make view selection more efficient and feasible for higher dimensional data. Further, it was experimentally found that PVGA trades significant improvement in time to evaluate all views with a slight drop in the quality of views selected for materialization.