Materialized View Selection Using Simulated Annealing

A data warehouse is designed for the purpose of answering decision making queries. These queries are usually long and exploratory in nature and have high response time, when processed against a continuously expanding data warehouse leading to delay in decision making. One way to reduce this response time is by using materialized views, which store pre-computed summarized information for answering decision queries. All views cannot be materialized due to their exponential space overhead. Further, selecting optimal subset of views is an NP-Complete problem. Alternatively, several view selection algorithms exist in literature, out of which most are empirical or based on heuristics like greedy, evolutionary etc. It has been observed that most of these view selection approaches find it infeasible to select good quality views for materialization for higher dimensional data sets. In this paper, a randomized view selection algorithm based on simulated annealing, for selecting Top-K views from amongst all possible sets of views in a multidimensional lattice, is presented. It is shown that the simulated annealing based view selection algorithm, in comparison to the better known greedy view selection algorithm, is able to select better quality views for higher dimensional data sets.