Contextual case-based reasoning applied to a mobile device

Djamel Guessoum (Ecole de Technologie Superieure, Notre-Dame Ouest, Montreal, Quebec, Canada)
Moeiz Miraoui (Higher Institute of Applied Science and Technology, Universite de Gafsa, Gafsa, Tunisia)
Chakib Tadj (Ecole de Technologie Superieure, Notre-Dame Ouest, Montreal, Quebec, Canada)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Publication date: 4 September 2017

Abstract

Purpose

This paper aims to apply a contextual case-based reasoning (CBR) to a mobile device. The CBR method was chosen because it does not require training, demands minimal processing resources and easily integrates with the dynamic and uncertain nature of pervasive computing. Based on a mobile user’s location and activity, which can be determined through the device’s inertial sensors and GPS capabilities, it is possible to select and offer appropriate services to this user.

Design/methodology/approach

The proposed approach comprises two stages. The first stage uses simple semantic similarity measures to retrieve the case from the case base that best matches the current case. In the second stage, the obtained selection of services is then filtered based on current contextual information.

Findings

This two-stage method adds a higher level of relevance to the services proposed to the user; yet, it is easy to implement on a mobile device.

Originality/value

A two-stage CBR using light processing methods and generating context aware services is discussed. Ontological location modeling adds reasoning flexibility and knowledge sharing capabilities.

Keywords

Citation

Guessoum, D., Miraoui, M. and Tadj, C. (2017), "Contextual case-based reasoning applied to a mobile device", International Journal of Pervasive Computing and Communications, Vol. 13 No. 3, pp. 282-299. https://doi.org/10.1108/IJPCC-11-2016-0056

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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