Abstract
Over the last years, the data generated in Telecommunications Networks has reached unmanageable limits of information. Data Mining (DM) techniques have showed their advantages on helping to manage this information and transforming it in useful knowledge. However, due to the dynamics of the environment of Telecommunications Networks, the simple application or adaptation of DM techniques is not enough to obtain timely a deeper knowledge. In this paper, this problem is addressed by applying DM techniques in real time. First, we propose a methodology taking into account all the processes involved in transforming telecommunications data into information, and finally to knowledge. Second, we propose a framework for the utilization of Intelligent Agents to help the process of DM in real time. To illustrate our approach, we describe a real-life case study based on the integration of Intelligent Agents and DM technologies for obtaining in real time knowledge that is critical for managing telecommunication networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Weiss, G.M.: Predicting Telecommunication Equipment Failures from Sequences of Network Alarms. In: Kloesgen, W., Zytkow, J. (eds.) Handbook of Knowledge Discovery and Data Mining, pp. 891–896. Oxford University Press, Oxford (2001)
Garofalakis, M., Rastogi, R.: Data Mining Meets Network Management: The NEMESIS Project. In: ACM SIGMOD Int’l Workshop on Research Issues in Data Mining and Knowledge Discovery (May 2001)
Berry, M., Linoff, G.S.: Data Mining Techniques, 2nd edn. Wiley, Chichester (2004)
Weiss, G.M.: Data Mining in Telecommunications. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, pp. 1189–1201. Kluwer Academic Publishers, Dordrecht (2005)
Lee, W., et al.: Real Time Data Mining-based Intrusion Detection. In: The 2001 DARPA Information Survivability Conference and Exposition (DISCEX II), Anaheim, CA (June 2001)
Hatonen, K., et al.: Knowledge discovery from telecommunication network alarm databases. In: Proc. of the 12th Int. Conf. on Data Engineering, New Orleans, Louisiana, pp. 115–122. IEEE Computer Society Press, Los Alamitos (1996)
Klemettinen, M.: A Knowledge Discovery Methodology for Telecommunication Network Alarm Databases. PhD thesis, University of Helsinki, Finland (1999)
Lin, J., et al.: A symbolic representation of time series, with implications for streaming algorithms. In: Proc. of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, CA (June 2003)
Last, M., Klein, Y., Kandel, A.: Knowledge discovery in time series databases. IEEE Trans. SMC, Part B 31(1), 160–169 (2001)
Keogh, E., Lin, J., Fu, A.: HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In: Proc. of the 5th IEEE International Conference on Data Mining (ICDM 2005), Houston, Texas, Nov. 27-30, 2005, pp. 226–233 (2005)
Batyrshin, I., Sheremetov, L.: Perception based time series data mining with MAP transform. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 514–523. Springer, Heidelberg (2005)
Weiss, G.: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge (2000)
Rocha-Mier, L.E., Amy, B., Pellegrini, C.: Neural Networks for learning in a Collective Intelligence: Internet packet routing application. In: The 3rd Int. Conf. on Cognitive Science, Beijing, China (August 2001)
Gorodetsky, V., et al.: Multi-Agent Information Fusion: Methodology, Architecture and Software Tool for Learning of Object and Situation Assessment. In: Proc. of the 7th International Conference Fusion-04, Stockholm, Sweden, June 28-July 2, 2004, pp. 346–353 (2004)
Ng, Y.S., Srinivasan, R.: Multi-agent Framework for Fault Detection & Diagnosis in Transient Operations. In: Plesu, V., Agachi, P.S. (eds.) Proc. of the 17th European Symposium on Computer Aided Process Engineering, Bucharest, Romania, 27-30 May 2007, Elsevier, Amsterdam (2007)
Samtani, S., et al.: Recent Advances and Research Problems in Data Warehousing. In: Kambayashi, Y., et al. (eds.) ER Workshops 1998. LNCS, vol. 1552, pp. 81–92. Springer, Heidelberg (1999)
Frank, R.J., Davey, N., Hunt, S.P.: Time Series Prediction and Neural Networks. J. of Intelligent and Robotic Systems 31(1-3), 91–103 (2001)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)
Sims, C.A.: Macroeconomics and Reality. Econometrica 48 (1980)
Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernatics 22(3), 418–435 (1992)
Hassan, U., Soh, B.: Using adaptive web service multi agents for collaborative forecasting and countering bullwhip effect in SCM. In: Proc. of the Int. Conf. Integration of Knowledge Intensive Multi-Agent Systems, April 18-21, 2005, pp. 567–572 (2005)
Contreras, M., et al.: Design and implementation of a FIPA compliant Agent Platform in .NET. J. of Object Technology 3(3) (2004)
Sheremetov, L., Contreras, M.: Industrial Application Integration Using Agent-Enabled SSOA: CAPNET Case Study. In: Shen, W. (ed.) Information Technologies for Balanced Manufacturing Systems. IFIP Series, pp. 109–118. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Rocha-Mier, L.E., Sheremetov, L., Batyrshin, I. (2007). Intelligent Agents for Real Time Data Mining in Telecommunications Networks. In: Gorodetsky, V., Zhang, C., Skormin, V.A., Cao, L. (eds) Autonomous Intelligent Systems: Multi-Agents and Data Mining. AIS-ADM 2007. Lecture Notes in Computer Science(), vol 4476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72839-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-72839-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72838-2
Online ISBN: 978-3-540-72839-9
eBook Packages: Computer ScienceComputer Science (R0)