Abstract
View selection is one of the key problems in view materialization. Several algorithms exist in literature for view selection, most of them are greedy based. The greedy algorithms, in each iteration, select the most beneficial view for materialization. Most of these algorithms are focused around algorithm HRUA. HRUA exhibits high run time complexity. As a result, it becomes infeasible to select views for higher dimensions. This scalability problem is addressed by the greedy algorithm VRGA proposed in this paper. Unlike HRUA, VRGA selects views from a smaller search space, comprising of recommended views, instead of all the views in the lattice. This enables VRGA to select views efficiently for higher dimensional data sets. Further, experimental results show that VRGA, in comparison to HRUA, requires significantly lesser benefit computations, view evaluation time and memory. Alternatively, HRUA has a slight edge over VRGA as regards to the total cost of evaluating all the views.