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Increased Network Monitoring Support through Topic Modeling
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2949-4123
Huawei Technologies Sweden.ORCID iD: 0000-0002-9671-7676
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-6245-5850
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2973-3112
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2020 (English)In: International Journal of Information, Communication Technology and Applications, E-ISSN 2205-0930, Vol. 6, no 1Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
Australasian Association for Information and Communication Technology , 2020. Vol. 6, no 1
Keywords [en]
topic modelling, exploratory data analysis, anomaly detection, root cause identification, telecommunication networks, network performance monitoring
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-19532OAI: oai:DiVA.org:his-19532DiVA, id: diva2:1536937
Funder
Knowledge Foundation
Note

CC BY-NC-ND 4.0

Copyright © Australasian Association for Information and Communication Technology General permission to republish, but not for profit, all or part of this material is granted, under the Creative Commons Australian Attribution-NonCommercial-NoDerivs 4.0 Licence, provided that the copyright notice is given and that reference is made to the publication, to its date of issue, and to the fact that reprinting privileges were granted by permission of the Copyright holder.

Available from: 2021-03-12 Created: 2021-03-12 Last updated: 2021-04-26Bibliographically approved

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Steinhauer, H. JoeHelldin, ToveKarlsson, AlexanderMathiason, Gunnar

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