Characterizing and predicting mobile application usage


Keun-Woo Lim, Stefano Secci, Lionel Tabourier and Badis Tebbani

In Computer Communications, 2016, vol. 95, p. 82-94


In this paper, we propose data clustering techniques to predict temporal
characteristics of data consumption behavior of different mobile applications
via wireless communications. While most of the research on mobile data
analytics focuses on the analysis of call data records and mobility traces, our
analysis concentrates on mobile application usages, to characterize them and
predict their behavior. We exploit mobile application usage logs provided
by a Wi-Fi local area network service provider to characterize temporal behavior
of mobile applications. More specifically, we generate daily profiles
of “what” types of mobile applications users access and “when” users access
them. From these profiles, we create usage classes of mobile applications via
aggregation of similar profiles depending on data consumption rate, using
three clustering techniques that we compare. Furthermore, we show that
we can utilize these classes to analyze and predict future usages of each mobile application through progressive comparison using distance and similarity
comparison techniques. Finally, we also detect and exploit outlying behavior
in application usage profiles and discuss methods to efficiently predict them.

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