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Anomaly detection of event sequences using multiple temporal resolutions and Markov chains
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-9316-4842
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-8929-7220
Ericsson Research, SWE.
Ericsson Research, SWE.
2019 (English)In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116Article in journal (Refereed) Epub ahead of print
Abstract [en]

Streaming data services, such as video-on-demand, are getting increasingly more popular, and they are expected to account for more than 80% of all Internet traffic in 2020. In this context, it is important for streaming service providers to detect deviations in service requests due to issues or changing end-user behaviors in order to ensure that end-users experience high quality in the provided service. Therefore, in this study we investigate to what extent sequence-based Markov models can be used for anomaly detection by means of the end-users’ control sequences in the video streams, i.e., event sequences such as play, pause, resume and stop. This anomaly detection approach is further investigated over three different temporal resolutions in the data, more specifically: 1 h, 1 day and 3 days. The proposed anomaly detection approach supports anomaly detection in ongoing streaming sessions as it recalculates the probability for a specific session to be anomalous for each new streaming control event that is received. Two experiments are used for measuring the potential of the approach, which gives promising results in terms of precision, recall, F 1 -score and Jaccard index when compared to k-means clustering of the sessions. © 2019, The Author(s).

Place, publisher, year, edition, pages
Springer London , 2019.
Keywords [en]
Anomaly detection, Event sequences, Markov Chains, Multiple temporal resolutions, Video-on-demand
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18026DOI: 10.1007/s10115-019-01365-yScopus ID: 2-s2.0-85066031197OAI: oai:DiVA.org:bth-18026DiVA, id: diva2:1324926
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-06-17Bibliographically approved

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Boldt, MartinBorg, Anton

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