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Modeling instantaneous quality of experience using machine learning of model trees
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
2019 (English)In: 2019 11th International Conference on Quality of Multimedia Experience, QoMEX 2019, Institute of Electrical and Electronics Engineers Inc. , 2019Conference paper, Published paper (Refereed)
Abstract [en]

For service providers and operators, successful root cause analysis is essential for satisfactory service provisioning. However, reasons for sudden trend changes of the instantaneous Quality of Experience (QoE) may not always be immediately visible from underlying service- or network-level monitoring data. Thus, there is the challenge to pinpoint such moments of change in provisioning, and model the impact on instantaneous QoE, as a lead in root cause analysis. This work investigates the potential of Machine Learning (ML) of deriving time-interval-based models for instantaneous QoE ratings, obtained from a set of publicly available rating traces. In particular, the paper demonstrates the capability of the ML algorithm M5P to model trends of instantaneous QoE through model trees, consisting of piecewise linear functions over time. It is shown how and to which extent these functions can be used to estimate moments of change. Furthermore, the model trees support earlier assumptions about exponential shapes of instantaneous QoE over time as reactions to sudden changes of provisioning, such as video freezes. © 2019 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019.
Keywords [en]
M5P algorithm, Mean Opinion Score (MOS), Root cause analysis, Time dependency, Video freezes, Forestry, Machine learning, Multimedia systems, Piecewise linear techniques, Trees (mathematics), Mean opinion scores, Piece-wise linear functions, Quality of experience (QoE), Service provider, Service provisioning, Time interval, Quality of service
National Category
Communication Systems Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-18604DOI: 10.1109/QoMEX.2019.8743250ISI: 000482562000035Scopus ID: 2-s2.0-85068689152ISBN: 9781538682128 (print)OAI: oai:DiVA.org:bth-18604DiVA, id: diva2:1349786
Conference
11th International Conference on Quality of Multimedia Experience, QoMEX, Berlin, 5 June 2019 through 7 June 2019
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-13Bibliographically approved

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Fiedler, Markus

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
  • en-GB
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  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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