Abstract
Call Detail Records (CDR) is a valuable source of information; it opens new opportunities for mobile operator industries and maximize their revenues as well as it helps the community to raise its standard of living in many different ways. Nevertheless, we need to analyze CDR in order to extract its big value and detect abnormal costumers behaviors to help companies to develop their future plans. However the analysis of CDRs is a very complex process this because it has a huge volume of data. Therefore, In this paper we propose an approach that makes use of Neo4j for automatic analysis of CDRs. To achieve this we transformed the CDR data into neo4j and then we used cypher query language for performing an automatic analysis. A real case study was used to evaluate the proposed approach.
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References
Hoteit, S., Chen, G., Viana, A., Fiore, M.: Filling the gaps: on the completion of sparse call detail records for mobility analysis. In: The Eleventh ACM Workshop on Challenged Networks, pp. 45–50 (2016)
Elagib, S.B., Hashim, A.A.-H., Olanrewaju, R.F.: CDR analysis using big data technology. In: International Conference on Computing Control Networking Electronics and Embedded Systems Engineering (ICCNEEE), pp. 467–471, September 2015
Kedma, G., Guri, M., Sela, T., Elovici, Y.: Analyzing users’ web surfing patterns to trace terrorists and criminals. In: Intelligence and Security Informatics (ISI), pp. 143–145, June 2013
Fiadino, P., Ponce-Lopez, V., Antonio, J., Torrent-Moreno, M., D’Alconzo, A.: Call detail records for human mobility studies: taking stock of the situation in the “always connected era”. In: Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, pp. 43–48. ACM (2017)
Pestre, G., Letouzé, E., Zagheni, E.: The ABCDE of big data: assessing biases in call-detail records for development estimates. In: Annual World Bank Conference on Development Economics (2016)
Zhao, Z., Shaw, S.-L., Xu, Y., Yin, L.: Understanding the bias of call detail records in human mobility research. Int. J. Geograph. Inf. Sci. 30(9), 1738–1762 (2016)
Kim, H.K., Kim, T.E., Jo, C.M., Na, S.R., Jurn, J.S.: Abnormal behavior detection system considering error rate deviation of entire use behavior pattern during personalized connection period. U.S. Patent Application 15/006,498. Filed 26 January 2016
Vukotic, A., Watt, N., Abedrabbo, T., Fox, D., Partner, J.: Neo4j in action. Manning Publications Co., Greenwich (2014)
Kumar, M., Hanumanthappa, M., Suresh Kumar, T.V.: Crime investigation and criminal network analysis using archive call detail records. In: 2016 Eighth International Conference on Advanced Computing (ICoAC), pp. 46–50. IEEE (2017)
Miller, J.J.: Graph database applications and concepts with neo4j. In: Proceedings of the Southern Association for Information Systems Conference, Atlanta, GA, USA, vol. 2324, p. 36 (2013)
Van Bruggen, R.: Learning Neo4j. Packt Publishing Ltd, Birmingham (2014)
Holzschuher, F., Peinl, R.: Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j. In: Proceedings of the Joint EDBT/ICDT 2013 Workshops, pp. 195–204. ACM (2013)
Hung, S.-Y., Yen, D.C., Wang, H.-Y.: Applying data mining to telecom churn management. Exp. Syst. Appl. 31(3), 515–524 (2006)
Acknowledgments
This research was supported by the Research & Development (R&D) office at Almadar Aljadid Company. We thank our colleagues from R&D office at Almadar Aljadid Company who provided insight and expertise that greatly assisted the research.
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Geepalla, E., Abuhamoud, N., Abouda, A. (2018). Analysis of Call Detail Records for Understanding Users Behavior and Anomaly Detection Using Neo4j. In: Alenezi, M., Qureshi, B. (eds) 5th International Symposium on Data Mining Applications. Advances in Intelligent Systems and Computing, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-78753-4_7
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DOI: https://doi.org/10.1007/978-3-319-78753-4_7
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