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Silveira L.M.,Federal University of Minas Gerais | de Almeida J.M.,Federal University of Minas Gerais | Marques-Neto H.T.,Pontifical Catholic University of Minas Gerais | Sarraute C.,GranData | Ziviani A.,National Laboratory for Scientific Computing LNCC
Computer Communications | Year: 2016

The literature is rich in mobility models that aim at predicting human mobility. Yet, these models typically consider only a single kind of data source, such as data from mobile calls or location data obtained from GPS and web applications. Thus, the robustness and effectiveness of such data-driven models from the literature remain unknown when using heterogeneous types of data. In contrast, this paper proposes a novel family of data-driven models, called MobHet, to predict human mobility using heterogeneous data sources. Our proposal is designed to use a combination of features capturing the popularity of a region, the frequency of transitions between regions, and the contacts of a user, which can be extracted from data obtained from various sources, both separately and conjointly. We evaluate the MobHet models, comparing them among themselves and with two single-source data-driven models, namely SMOOTH and Leap Graph, while considering different scenarios with single as well as multiple data sources. Our experimental results show that our best MobHet model produces results that are better than or at least comparable to the best baseline in all considered scenarios, unlike the previous models whose performance is very dependent on the particular type of data used. Our results thus attest the robustness of our proposed solution to the use of heterogeneous data sources in predicting human mobility. © 2016. Source


Leo Y.,University of Lyon | Busson A.,University of Lyon | Sarraute C.,GranData | Fleury E.,University of Lyon
Computer Communications | Year: 2016

Cellular technologies are evolving quickly to constantly adapt to new usage and tolerate the load induced by the increasing number of phone applications. Understanding the mobile traffic is thus crucial to refine models and improve experiments. In this context, one has to understand the temporal activity of a user and the user movements. At the user scale, the usage is not only defined by the amount of calls but also by the user's mobility. At a higher level, the base stations have a key role on the quality of service. In this paper, we analyze a very large Call Detail Records (CDR) over 12 months in Mexico. It contains 8 millions users and 5 billions of call events. Our first contribution is the study call duration and inter-arrival time parameters. Then, we assess user movements between consecutive calls (switching from a station to another one). Our study suggests that user mobility is pretty dependent on user activity. Furthermore, we show properties of the inter-call mobility by making an analysis of the call distribution. © 2016 Elsevier B.V. Source


Oliveira E.M.R.,Ecole Polytechnique - Palaiseau | Oliveira E.M.R.,French Institute for Research in Computer Science and Automation | Viana A.C.,French Institute for Research in Computer Science and Automation | Naveen K.P.,French Institute for Research in Computer Science and Automation | Sarraute C.,GranData
2015 IEEE International Conference on Pervasive Computing and Communications, PerCom 2015 | Year: 2015

Understanding mobile data traffic demands is crucial to the evaluation of strategies addressing the problem of high bandwidth usage and scalability of network resources, brought by the pervasive era. In this paper, we conduct the first detailed measurement-driven modeling of smartphone subscribers' mobile traffic usage in a metropolitan scenario. We use a large-scale dataset collected inside the core of a major 3G network of Mexico's capital. We first analyse individual subscribers routine behavior and observe identical usage patterns on different days. This motivates us to choose one day for studying the subscribers' usage pattern (i.e., 'when' and 'how much' traffic is generated) in detail. We then classify the subscribers in four distinct profiles according to their usage pattern. We finally model the usage pattern of these four subscriber profiles according to two different journey periods: peak and non-peak hours. We show that the synthetic trace generated by our data traffic model consistently imitates different subscriber profiles in two journey periods, when compared to the original dataset. © 2015 IEEE. Source


Sarraute C.,GranData | Blanc P.,University of Buenos Aires | Burroni J.,GranData
ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining | Year: 2014

Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper we focus on the population of Mexican mobile phone users. Our first contribution is an observational study of mobile phone usage according to gender and age groups. We were able to detect significant differences in phone usage among different subgroups of the population. Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We provide details of the methodology and show experimental results on a real world dataset that involves millions of users. © 2014 IEEE. Source


Ponieman N.B.,GranData | Salles A.,University of Buenos Aires | Sarraute C.,GranData
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 | Year: 2013

The massive amounts of geolocation data collected from mobile phone records has sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, focusing in particular in the cases of urban commute and major sports events. We illustrate how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user's location. Copyright 2013 ACM. Source

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