A reduced lattice greedy algorithm for selecting materialized views

Abstract

View selection generally deals with selecting an optimal set of beneficial views for materialization subject to constraints like space, response time, etc. The problem of view selection has been shown to be in NP. Several greedy view selection algorithms exist in literature, most of which are focused around algorithm HRU, which uses a multidimensional lattice framework to determine a good set of views to materialize. Algorithm HRU exhibits a high run time complexity. One reason for it may be the high number of re-computations of benefit values needed for selecting views for materialization. This problem has been addressed by the algorithm Reduced Lattice Greedy Algorithm (RLGA) proposed in this paper. Algorithm RLGA selects beneficial views greedily over a reduced lattice, instead of the complete lattice as in the case of HRU algorithm. The use of the reduced lattice, containing a reduced number of dependencies among views, would lead to overall reduction in the number of re-computations required for selecting materialized views. Further, it was also experimentally found that RLGA, in comparison to HRU, was able to select fairly good quality views with fewer re-computations and an improved execution time. © 2009 Springer Berlin Heidelberg.