Unsupervised analysis for residential user experience quality inference

• Gustavo H. A. Santos UFRJ
• Gabriel Mendonça UFRJ
• Edmundo de Souza e Silva UFRJ
• Rosa M. M. Leão UFRJ
• Daniel S. Menasché UFRJ

DOI: https://doi.org/10.5753/sbrc.2019.7415

Abstract

Assessing the quality of experience of residential users is of great interest to ISPs. However, obtaining perceived QoE is costly, making it difficult to use supervised classifiers. This paper proposes a method based on unsupervised machine learning that detects statistical patterns in time series from the detection of change points and the spatiotemporal correlation of QoS measurement results. We exemplify the application of the method to a set of actual data, showing that the model results reflect a user QoE metric obtained from technical calls made to the call center. Finally, we evaluated the accuracy of the online execution of the method.

Keywords: Internet Service Providers, Quality of Experience, Machine Learning

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