Abstract
The tremendous amount of data are generated and collected by telecommunication companies. These data include call detail data which describe the calls traversing the telecommunication networks as well as network and customer data which mainly describe incomes of telecommunication companies. The amount of data is so huge that manual analysis of these data is impossible. The need to automatically handle such large volumes of data has led to the development of special algorithms and technologies such as data mining, intelligent computer agents, knowledge-based expert systems, etc. Telecommunication companies are strongly interested not only in identifying fraudulent phone calls and identifying network faults but also in forecasting the preferred directions of customer calls or the incomes of the companies. The paper presents a communication real anomaly detection framework, which uses data mining technologies using OLAP cube built for telecommunication data.
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References
Sumathi, S., Sivanandam, S.N.: Introduction to Data Mining and its Applications. In: Part 24. Data Mining in Telecommunications and Control. Studies in Computational Intelligence (SCI), vol. 29, pp. 615–627 (2006)
Weiss G.M.: Data Mining in Telecommunications. In: Maimon, O., Rokach, L.: Data Mining and Knowledge Discovery Handbook, ch. 56, pp. 1189–1201. Springer, US (2005)
Sumathi, S.: Data mining and data warehousing. In: Sumathi, S., Esakkirajan, S. (eds.) Fundamentals of Relational Database Management Systems. Studies in Computational Intelligence (SCI), vol. 47, pp. 415–475 (2007)
Berry, M.J.A., Linoff, G.S.: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 2nd edn. John Wiley & Sons, Chichester (2004)
Berry, M.W., Castellanos, M. (eds.): Survey of Text Mining II. Clustering, Classification, and Retrieval. Springer, Heidelberg (2007)
Alba, E., Talbi, E., Zomaya, A.Y.: Nature-inspired distributed computing. Computer Communications 30, 653–655 (2007)
Rocha-Mier, L.E., Sheremetov, L., Batyrshin, I.: Intelligent agents for real time data mining in telecommunications networks. In: Gorodetsky, V., Zhang, C., Skormin, V.A., Cao, L. (eds.) AIS-ADM 2007. LNCS (LNAI), vol. 4476, pp. 138–152. Springer, Heidelberg (2007)
Kordon, A.K.: Applying Computational Intelligence. In: How to Create Value. Part 6. Swarm Intelligence: The Benefits of Swarms, pp. 145–174. Springer, Berlin (2010)
Patcha, A., Park, J.-M.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks 51, 3448–3470 (2007)
Zhang, W., Yang, Q., Geng, Y.: A survey of anomaly detection methods in networks. In: International Symposium on Computer Network and Multimedia Technology, pp. 1–3 (2009)
Wang, X.-F., Zhou, J.-L., Yu, S.-S., Cai, L.-Z.: Data mining methods for anomaly detection of HTTP request exploitations. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3614, pp. 320–323. Springer, Heidelberg (2005)
Kumpulainen, P., Hätönen, K.: Local anomaly detection for mobile network monitoring. Information Sciences 178, 3840–3859 (2008)
Lazarevic, A., Srivastava, N., Tiwari, A., Isom, J., Oza, N., Srivastava, J.: Theoretically optimal distributed anomaly detection. In: IEEE International Conference on Data Mining Workshops, pp. 515–520 (2009)
Rabatel, J., Bringay, S., Poncelet, P.: SO_MAD: SensOr Mining for Anomaly Detection in railway data. In: Perner, P. (ed.) Advances in Data Mining. Applications and Theoretical Aspects. Lecture Notes in Computer Science, LNAI, vol. 5633, pp. 191–205. Springer, Heidelberg (2009)
Wrembel, R., Koncilia, C. (eds.): Data Warehouses and OLAP: Concepts, Architectures, and Solutions. Idea Group Inc., Hershey (2007)
O’Brien, J.A., Marakas, G.M.: Management Information Systems, 9th edn. McGraw-Hill/Irwin, Boston (2009)
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Choroś, K. (2010). Real Anomaly Detection in Telecommunication Multidimensional Data Using Data Mining Techniques. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6421. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16693-8_2
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DOI: https://doi.org/10.1007/978-3-642-16693-8_2
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