Towards an Automatic Extraction of Smartphone Users' Contextual Behaviors

Abstract : This paper presents a new method for automatically extracting smartphone users' contextual behaviors from the digital traces collected during their interactions with their devices. Our goal is in particular to understand the impact of users' context (e.g., location, time, environment, etc.) on the applications they run on their smartphones. We propose a methodology to analyze digital traces and to automatically identify the significant information that characterizes users' behaviors. In earlier work, we have used Formal Concept Analysis and Galois lattices to extract relevant knowledge from heterogeneous and complex contextual data; however, the interpretation of the obtained Galois lattices was performed manually. In this article, we aim at automating this interpretation process, through the provision of original metrics. Therefore our methodology returns relevant information without requiring any expertise in data analysis. We illustrate our contribution on real data collected from volunteer users.
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Communication dans un congrès
IEEE 10th International Conference on Research Challenges in Information Science (RCIS), Jun 2016, Grenoble, France. 2016, 〈www.rcis-conf.com/rcis2016/〉
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Soumis le : vendredi 27 mai 2016 - 16:57:12
Dernière modification le : samedi 19 mai 2018 - 15:50:02
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Ali Jaffal, Bénédicte Le Grand. Towards an Automatic Extraction of Smartphone Users' Contextual Behaviors. IEEE 10th International Conference on Research Challenges in Information Science (RCIS), Jun 2016, Grenoble, France. 2016, 〈www.rcis-conf.com/rcis2016/〉. 〈hal-01322806〉

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