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Identifying abnormal pattern in cellular communication flows
Goergen, David; Mendiratta, Veena; State, Radu et al.
2013In Proceedings of IPTComm 2013
Peer reviewed
 

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Keywords :
Call data record analysis; Big Data processing; call pattern detection
Abstract :
[en] Analyzing communication flows on the network can help to improve the overall quality it provides to its users and allow the operators to detect abnormal patterns and react accordingly. In this paper we consider the analysis of large volumes of cellular communications records. We propose a method that detects abnormal communications events covering call data record volumes, comprising a country-level data set. We detect patterns by calculating a weighted average using a sliding window with a fixed period and correlate the results with actual events happening at that time. We are able to successfully detect several events using a data set provided by a mobile phone operator, and suggest examples of future usage of the outcome such as real time pattern detection and possible visualisation for mobile phone operators.
Disciplines :
Computer science
Author, co-author :
Goergen, David ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Mendiratta, Veena
State, Radu  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Engel, Thomas ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Language :
English
Title :
Identifying abnormal pattern in cellular communication flows
Publication date :
October 2013
Event name :
7th International Conference on Principles, Systems and Applications of IP Telecommunications
Event organizer :
Illinois Institute of Technology
Event place :
Chicago, United States
Event date :
October 15-17, 2013
Audience :
International
Main work title :
Proceedings of IPTComm 2013
Publisher :
ACM
ISBN/EAN :
978-1-4503-2672-8
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 05 February 2014

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