Authors: T.V. Vijay Kumar; Biri Arun
Addresses: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi-110067, India ' School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi-110067, India
Abstract: Economists in the post-industrial era had long realised that data, information and knowledge are the key capital of any organisation. Presently, almost every enterprise maintains their data in a data warehouse. This helps the analyst in accessing critical business information in real time using online analytical processing (OLAP) tools. Materialised views have been the popular mode used to achieve very fast OLAP operations. Selecting appropriate sets of optimal views, from amongst all possible views, is an NP-complete problem. In this paper, the bee colony optimisation (BCO) meta-heuristic, which is inspired by the foraging behaviour of bees in nature, has been adapted to address the view selection problem. In this regard, a BCO-based view selection algorithm (BCOVSA), that selects the Top-K views from a multidimensional lattice, has been proposed. The experimental results show that BCOVSA, in comparison to the most fundamental greedy view selection algorithm HRUA, is able to select comparatively better quality of views.
Keywords: data warehouse; materialised view selection; swarm intelligence; bee colony optimisation; BCO; online analytical processing; OLAP; artificial bee colony; ABC; business information systems.
International Journal of Business Information Systems, 2016 Vol.22 No.3, pp.280 - 301
Received: 27 Nov 2014
Accepted: 08 Jan 2015
Published online: 05 Jun 2016 *