Authors: Nachida Rezoug; Omar Boussaid; Fahima Nader
Addresses: National Computer Science Engineering School, ESI, Oued Smar, Algiers, Algeria ' Lyon 2 University, 5, Avenue Pièrre Mendès, France ' National Computer Science Engineering School, ESI, Oued Smar, Algiers, Algeria
Abstract: We propose in this paper a knowledge-based framework for a mobile OLAP. Its main goal is to allow decision-makers to, efficiently, access datasets in OLAP system anywhere and anytime. The challenge of this work is to be able to improve decisional performances while overcoming capability context. To achieve this goal, the framework integrates in a systematic, generic and extensible way, knowledge in the context-aware recommender process, on one hand. On the other hand, it proposes contextual recommendations. For that purpose, the context-aware recommender system exploits the knowledge extracted (profile in a particular situation 'contextual profile') automatically and the current user's contextual profile to compute a list of contextual recommendations (analysis) adapted to the capability context. We conducted a set of experiments to evaluate the performance of our knowledge-based framework. The results are encouraging and show that our framework contributes significantly to improve mobile OLAP navigation.
Keywords: mobile OLAP; online analytical processing; contextual recommender systems; data mining; K2; OLAM; CBR; case-based reasoning; knowledge-based systems; KBS; decision support systems; DSS; contextual recommendations.
International Journal of Decision Support Systems, 2015 Vol.1 No.1, pp.72 - 114
Received: 18 Jul 2013
Accepted: 23 Feb 2014
Published online: 18 Mar 2015 *