Boulaalam, O. and Aghoutane, B. and Ouadghiri, D.E. and Moumen, A. and Cheikh Malinine, M.L. (2018) Proposal of a Big data System Based on the Recommendation and Profiling Techniques for an Intelligent Management of Moroccan Tourism. In: UNSPECIFIED.

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Abstract

Big data has received high attention, and its added value is now recognized by many governments and industries, tourism is involved. In fact, the systematic and strategic analysis of big data exponentially generated by tourists allows to extract valuable insight for the travel industry. It provides a better understanding of customers' behaviors and preferences to make better and faster decisions. Nonetheless, the tourism industry in morocco has not yet exploited the potential benefits to be gained from big data, but the awareness of its importance exists. However, the rapid diffusion of new technologies in Moroccan tourism have generated new needs and challenges to operate tourism information about the territory effectively, anticipate the tourist behavior and provide personalized services. To address these issues, we propose a new system Big Data to exploit mass data created by tourists, to recommend targeted and personalized tourism offer according to users' profile, assist tourism stakeholders to make the adequate strategic decisions and thus promote Moroccan tourism. The objective of this study is to highlight the usefulness of big data in the tourism domain and then present a general overview of our big data system. © 2018 The Authors. Published by Elsevier Ltd.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Data mining; Information management; Ubiquitous computing, Big data systems; Intelligent management; Morocco; Personalized service; Personalized tourisms; Strategic analysis; Strategic decisions; tourism, Big data
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46
URI: http://eprints.umi.ac.ma/id/eprint/2407

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