Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Measuring serendipity: connecting people, locations and interests in a mobile 3G network
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Proceedings of the 8th international conference on Mobile systems, applications, and services
A first look at mobile hand-held device traffic
PAM'10 Proceedings of the 11th international conference on Passive and active measurement
A Modular Machine Learning System for Flow-Level Traffic Classification in Large Networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Using heterogeneous data sources collected from one of the largest 3G cellular networks in the US over three months, in this paper we investigate the usage patterns of mobile data users. We observe that data usage across mobile users are highly uneven. Most of the users access data services occasionally, while a small number of heavy users contribute to a majority of data usage in the network. We apply statistical tools, such as Markov model and tri-nonnegative matrix factorization, to characterize data users. We find that the intensive usage from heavy users can be attributed to a small number of applications, mostly video/audio streaming, data-intensive mobile apps, and popular social media sites. Our analysis provides a fine-grained categorization of data users based on their usage patterns and sheds light on the potential impact of different users on the cellular data network.