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Classifying Call Profiles in Large-scale Mobile Traffic Datasets

Diala Naboulsi 1 Razvan Stanica 1, * Marco Fiore 1, 2 
* Corresponding author
1 URBANET - Réseaux capillaires urbains
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : Cellular communications are undergoing significant evolutions in order to accommodate the load generated by increasingly pervasive smart mobile devices. Dynamic access network adaptation to customers' demands is one of the most promising paths taken by network operators. To that end, one must be able to process large amount of mobile traffic data and outline the network utilization in an automated manner. In this paper, we propose a framework to analyze broad sets of Call Detail Records (CDRs) so as to define categories of mobile call profiles and classify network usages accordingly. We evaluate our framework on a CDR dataset including more than 300 million calls recorded in an urban area over 5 months. We show how our approach allows to classify similar network usage profiles and to tell apart normal and outlying call behaviors.
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Contributor : Razvan Stanica Connect in order to contact the contributor
Submitted on : Wednesday, June 11, 2014 - 8:08:17 PM
Last modification on : Thursday, January 20, 2022 - 5:28:01 PM


  • HAL Id : hal-01005050, version 1



Diala Naboulsi, Razvan Stanica, Marco Fiore. Classifying Call Profiles in Large-scale Mobile Traffic Datasets. INFOCOM - 33rd Annual IEEE International Conference on Computer Communications, Apr 2014, Toronto, Canada. ⟨hal-01005050⟩



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