Increased Network Monitoring Support through Topic Modelling

Main Article Content

Joe Steinhauer https://orcid.org/0000-0003-2949-4123
Anders Åhlén https://orcid.org/0000-0002-9671-7676
Tove Helldin
Alexander Karlsson https://orcid.org/0000-0003-2973-3112
Gunnar Mathiason https://orcid.org/0000-0001-7106-0025

Keywords

Topic Modelling, Exploratory Data Analysis, Anomaly Detection, Root Cause Detection, Telecommunication Networks, Network Performance Monitoring

Abstract

To ensure that a wireless telecommunication system is reliably functioning at all times, root-causes of potential network failures need to be identified and remedied, ideally before a noticeable network performance degradation occurs. Network operators are today observing a multitude of key performance indicators (KPIs) and are notified of possible network problems through alarms issued by different parts of the network. However, the number of cascading alarms together with the number of observable KPIs are easily overwhelming the operator’s cognitive capacity. In this paper we show how exploratory data analysis and machine learning, in particular topic modelling, can assist the operator when monitoring network performance and identifying anomalous network behaviour as well as supporting the operator’s analysis of the anomaly and identification of its root-cause.

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References

Blei, D.M., Ng, A.Y., Jordan, M.I. (2003). Latent Drichlet Allocation. Jour. of Machine Learning Research 3(Jan), 993-1022.
Blei D. M. (2012). Probabilistic topic models. Commun. ACM 55(4):77–84.
Chandola, V., Banerjee, A., Kumar, V.(2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41(3).
Chaney A.J.B., Blei D.M. (2012). Visualizing topic models. 6th Inter. AAAI conf. on weblogs and social media.
Chang, J., Gerrish, S., Wang, C., Boyd-graber, J.L., Blei, D.M. (2009). Reading tea leaves: how humans interpret topic models. In: Bengio Y, Schuurmans D, Laferty JD, Williams CKI, Culotta A (eds) Advances in neural information processing systems 22, Curran Associates, Inc., pp 288–296.
Chuang J., Manning C.D., Heer J. (2012). Termite: Visualization techniques for assessing textual topic models. In: Proc. of the Inter. working conf. on advanced visual interfaces, ACM, pp 74–77.
Cui W., Liu S., Tan L., Shi C., Song Y., Gao Z., Qu H., Tong X. (2011). Textlow: towards better understanding of evolving topics in text. IEEE Trans Vis Comput Graph 17(12):2412–2421.
Dou W., Wang X., Chang R., Ribarsky W. (2011). Paralleltopics: A probabilistic approach to exploring document collections. In: IEEE conf. on visual analytics science and technology (VAST), IEEE, pp 231–240.
Endsley M.R., Jones D.G. (2004). Designing for situation awareness: an approach to user-centered design. CRC Press, Boca Raton.
Endsley, M., Kiris, E. (1995). The out-of-the-loop performance problem and level of control in automation. Human Factors: The Jour. of the Human Factors and Ergonomics Society 37 pp 381-394.
Gardner M.J., Lutes J., Lund J., Hansen J., Walker D., Ringger E., Seppi K. (2010). The topic browser: An interactive tool for browsing topic models. In: NIPS workshop on challenges of data visualization, vol 2.
Gretarsson B., O’donovan J., Bostandjiev S., Höllerer T., Asuncion A., Newman D., Smyth P. (2012). Topicnets: visual analysis of large text corpora with topic modeling. ACM Trans Intell Syst Technol (TIST) 3(2):23.
Havre S., Hetzler E., Whitney P., Nowell L. (2002). Themeriver: visualizing thematic changes in large document collections. IEEE Trans Vis Comput Graph 8(1):9–20.
Helldin, T., Steinhauer, H.J., Karlsson, A., Mathiason, G. (2018). Situation awareness in telecommunication networks using topic modeling. In: Submitted to the 21st Inter. Conf. on Information Fusion.
Huhnstock, N.A., Karlsson, A., Riveiro, M., Steinhauer, H.J. (2019). An Infinite Replicated Softmax Model for Topic Modeling. In: Conference on Modeling Decisions for Artificial Intelligence (MDAI).
Imran, A., Zoha, A. (2014). Challenges in 5G: how to empower SON with big data for enabling 5G. Network, IEEE 28(6), 27-33.Aliu, O.G., Imran, A., Imran, M.A., Evans, B. (2013). A survey of self organisation in future cellular networks. Communications Surveys & Tutorials, IEEE 15(1), 336-361.
Klein L.F., Eisenstein J., Sun I. (2015). Exploratory thematic analysis for digitized archival collections. Digital scholarship in the humanities, pp 130–141.
Koh L.C., Slingsby A., Dykes J., Kam T.S. (2011). Developing and applying a user-centered model for the design and implementation of information visualization tools. In: 15th Inter. conf. on information visualisation, IEEE, pp 90–95.
Liu S., Zhou M.X., Pan S., Song Y., Qian W., Cai W., Lian X. (2012). Tiara: Interactive, topic-based visual text summarization and analysis. ACM Trans Intell Syst Technol (TIST) 3(2):25.
Livnat, Y., Agutter, J., Moon, S., Erbacher, R.F., Foresti, S. (2005). A visualization paradigm for network intrusion detection. In: Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop. pp 92-99.
McAfee, A., Brynjolfsson, E. (2012). Big data: the management revolution. Harvard business review 90: 60–68.
Mimno, D., Wallach, H. M., Talley, E., Leenders, M., McCallum, A. (2011). Optimizing semantic coherence in topic models. In: Proceedings of the conference on empirical methods in natural language processing, association for computational linguistics, EMNLP ’11, pp 262–272.
Munzner, T. (2009). A nested model for visualization design and validation. IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6.
Nilsson M.,Ziemke, T. (2006) Rethinking level 5: Distributed cognition and information fusion. In: International Conference on Information Fusion.
Offermann, P., Levina, O., Schönherr, M., Bub, U. (2009). “Outline of adesign science research process,” in Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology. ACM, p. 7.
Pirinen, P. (2014). A brief overview of 5G research activities. In: 1st Inter. Conf. on 5G for Ubiquitous Connectivity (5GU). pp 17-22. IEEE.
Sagiroglu, S., Sinanc, D. (2013). Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on, pp 42–47.
Sedlmair, M., Meyer, M., Munzner, T. (2012). “Design study methodology: Reflections from the trenches and the stacks,” IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 12, pp 2431–2440.
Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information visualizations. in Proceedings of the IEEE Symposium on Visual Languages.
Sievert C., Shirley K.E. (2014). LDAvis: A method for visualizing and interpreting topics. In: Proc. of the workshop on interactive language learning, visualization, and interfaces, pp 63–70.
Sievert C., Shirley K. (2015). LDAvis: interactive visualization of topic models. https ://CRAN.R-proje ct.org/packa ge=LDAvis.
Simon, H.A. (1982). Models of Bounded Rationality: Behavioural Economics and Business Organisation. Cambridge, Mass, The MIT Press.
Smith A.M., Hawes T., Myers M. (2014). Hierarchie: Interactive visualization for hierarchical topic models. In: Proc. of the workshop on interactive language learning, visualization, and interfaces, pp 71–78.
Steinhauer, H. J., Helldin, T., Mathiason, G., Karlsson A. (2019). Topic Modeling for Anomaly Detection in Telecommunication Networks. Journal of Ambient Intelligence and Humanized Computing.
Tan, P-N., Steinbach, M., Kumar, V. (2006). Introduction to Data Mining. Addison-Wesley, Boston. ISBN 0-321-32136-7.
Teh, Y. W., Jordan, M. I., Beal, M. J., Blei, D. M. (2005). Sharing clusters among related groups:Hierarchical Dirichlet processes. In Advances in neural information processing systems, pp 1385-1392.
Tukey, J.W. (1962) The Future of Data Analysis. Annals of Mathematical Statistics 33(1), 1-67.
Vredenburg, K., Mao, J.-Y., Smith P. W., and Carey, T. (2002). “A survey of user-centered design practice,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ser. CHI ’02. New York, NY, USA: ACM, pp 471–478. [Online]. Available: http://doi.acm.org.libraryproxy.his.se/10.1145/503376.503460.
Xiong L., Poczos B., Schneider J., Connolly A., Van der Plas J. (2011). Hierarchical probabilistic models for group anomaly detection. In: Gordon G, Dunson D, Dudík M (eds) Proce. of the 14th Inter. Conf. on artificial intelligence and statistics, PMLR, Fort Lauderdale, FL, USA, Proc. of machine learning research, vol 15, pp 789–797.