Usage pattern analysis of smartphones

Kang, Joon-Myung; Seo, Sin-seok; Hong, James Won‐Ki

doi:10.1109/apnoms.2011.6077030

Cited by 57 publications

(23 citation statements)

References 12 publications

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“…Various researchers have made some analysis regarding usage of applications and data using smartphones such as [16] and [17]. These works are different from existing work in the sense that they collect the logs from the user's smartphone and analyze the application usage behavior of individual users.…”

Section: Related Workmentioning

confidence: 99%

Characterizing and predicting mobile application usage

Lim

Secci

Tabourier

et al. 2016

Computer Communications

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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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Section: Related Workmentioning

confidence: 99%

Characterizing and predicting mobile application usage

Lim

Secci

Tabourier

et al. 2016

Computer Communications

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“…Their mainly focus lies in the integration of generic context sensors, especially the location and visual context. On the other hand, Do [3] and Kang et al [5] try to mine large-scale Apps usage data to better understand the user behaviors. They have not, however, integrated any contextual information.…”

Section: Related Workmentioning

confidence: 99%

Predicting mobile application usage using contextual information

Huang

Zhang

et al. 2012

Proceedings of the 2012 ACM Conference on Ubiquitous Computing

112

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As the mobile applications become increasing popular, people are installing more and more Apps on their smart phones. In this paper, we answer the question whether it is feasible to predict which App the user will open. The ability for such prediction can help pre-loading the right Apps to the memory for faster execution or help floating the desired Apps to the home screen for quicker launch. We explored a variety of contextual information, such as last used App, time, location, and the user profile, to predict the user's App usage using the MDC dataset. We present three findings from our studies. First, the contextual information can be used to learn the pattern of user's App usage and to predict App usage effectively. Second, for the MDC dataset, the correlation between sequentially used Apps has a strong contribution to the prediction accuracy. Lastly, the linear model is more effective than the Bayesian model to combine all contextual information and for such predictions.

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“…Falaki et al [4] studied the usage pattern of cellular phones in the perspective of energy efficient scheduling algorithm design for mobile systems. Kang et al [5] also studied the usage pattern of smartphones for network traffic management study. Our smartphone addiction management system (SAMS) is presented in the previous work [6] and general usages pattern from Korean smartphone users is studied in [8].…”

Section: Introductionmentioning

confidence: 99%