PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Furletti B., Gabrielli L., Renso C., Rinzivillo S. Analysis of GSM calls data for understanding user mobility behavior. In: Big Data 2013 - 2013 IEEE International Conference on Big Data (Santa Clara Marriott, CA, USA, 6-9 October 2013). Proceedings, pp. 550 - 555. Xiaohua Hu, Tsau Young Lin, Vijay Raghavan, Benjamin Wah, Ricardo Baeza-Yates, Geoffrey Fox, Cyrus Shahabi, Matthew Smith, Qiang Yang, Rayid Ghani, Wei Fan, Ronny Lempel, Raghunath Nambiar (eds.). IEEE, 2013.
 
 
Abstract
(English)
This information about our GSM calls is stored by the TelCo operator in large volumes and with strict privacy constraints making it challenging the analysis of these fingerprints for inferring mobility behavior. This paper proposes a strategy for mobility behavior identification based on aggregated calling profiles of mobile phone users. This compact representation of the user call profiles is the input of the mining algorithm for automatically classifying various kinds of mobility behavior. A further advantage of having defined the call profiles is that the analysis phase is based on summarized privacy-preserving representation of the original data. We show how these call profiles permit to design a two step process - implemented into a system - based on a bootstrap phase and a running phase for classifying users into behavior categories. We evaluated the system in two case studies where individuals are classified into residents, commuters and visitors. We conclude the paper with a discussion which emphasizes the role of the call profiles for the design of a new collaboration model between data provider and data analyst.
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6691621&isnumber=6690588
DOI: 10.1109/BigData.2013.6691621
Subject GSM
Mobile phone data
Data analysis
H.2.8 Database Applications. Data mining
68U35 Information systems (hypertext navigation, interfaces, decision support, etc.)


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