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Research issues in mining user behavioral rules for context-aware intelligent mobile applications

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Abstract

Context awareness in smart mobile applications is a growing area of study because of its intelligence in the applications. To build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key. However, to mine these rules, a number of issues, such as the quality of smartphone data, understanding the relevancy of contexts, discretization of continuous contextual data, discovery of useful behavioral rules of individuals and their ordering, knowledge-based interactive post-mining for semantic understanding, and dynamic updating and management of rules according to their present behavior, are investigated. In this paper, we briefly discuss these issues and their potential solution directions for mining individuals’ behavioral rules, for the purpose of building various context-aware intelligent mobile applications. We also summarize a number of real-life rule-based applications that intelligently assist individual smartphone users according to their behavioral rules in their daily activities.

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Acknowledgements

The author would like to thank Prof. Jun Han, Swinburne University of Technology, Australia, Dr. Alan Colman, Swinburne University of Technology, Australia, and Dr. Ashad Kabir, Charles Sturt University, Australia, for their relevant discussions.

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  1. Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC, 3122, Australia

    Iqbal H. Sarker

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Sarker, I.H. Research issues in mining user behavioral rules for context-aware intelligent mobile applications. Iran J Comput Sci 2, 41–51 (2019). https://doi.org/10.1007/s42044-018-0026-1

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