Abstract
The data warehouse is a key data repository of any business enterprise that stores enormous historical data meant for answering analytical queries. These queries need to be processed efficiently in order to make efficient and timely decisions. One way to achieve this is by materializing views over a data warehouse. An n-dimensional star schema can be mapped into an n-dimensional lattice from which Top-K views can be selected for materialization. Selection of such Top-K views is an NP-Hard problem. Several metaheuristic algorithms have been used to address this view selection problem. In this paper, a swap operator-based particle swarm optimization technique has been adapted to address such a view selection problem.
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