Quality materialised view selection using quantum inspired artificial bee colony optimisation

The availability of huge volumes of digital data and powerful computers has facilitated the extraction of information, knowledge and wisdom for decision support system. The information value is solely dependent on data quality. Data warehouse provides quality data; it is required that it responds to queries within seconds. But on account of steadily growing data warehouse, the query response time is generally in hours and weeks. Materialised view is an efficient approach to facilitate timely extraction of information and knowledge for strategic business decision making. Selecting an optimal set of views for materialisation, referred to as view selection, is a NP complete problem. In this paper, a quantum inspired artificial bee colony algorithm is proposed to address the view selection problem. Experimental results show that the proposed algorithm significantly outperforms the fundamental algorithm for view selection, HRUA and other view selection algorithms like ABC, MBO, HBMO, BCOc, BCOi and BBMO.