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Title: Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity
Authors: Skaperas, Sotiris
Mamatas, Lefteris
Chorti, Arsenia
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Engineering and technology::Other engineering and technologies
Keywords: Content popularity dynamics detection
change point analysis
variance change detection
volatility detection
Issue Date: 2020
Publisher: IEEE
Source: IEEE Access
Volume: 8
First Page: 30445
Last Page: 30457
Abstract: As video content is responsible for more than 70% of the global IP traffic, related resource allocation approaches, e.g., using content caching, become increasingly important. In this context, to avoid under-provisioning, it is important to rapidly detect and respond to changes in content popularity dynamics, including volatility, i.e., changes in the second order moment of the underlying process. In this paper, we focus on the early identification of changes in the variance of video content popularity, which we address as a statistical change point (CP) detection problem. Unlike changes in the mean that can be well captured by non-parametric statistical approaches, to address this more demanding problem, we construct a hypothesis test that uses in the test statistic both parametric and non-parametric approaches. In the context of parametric models, we consider linear, in the form of autoregressive moving average (ARMA), and, nonlinear, in the form of generalized autoregressive conditional heteroskedasticity (GARCH) processes. We propose an integrated algorithm that combines off-line and on-line CP schemes, with the off-line scheme used as a training (learning) phase. The algorithm is first assessed over synthetic data; our analysis demonstrates that non parametric and GARCH model based approaches can better generalize and are better suited for content views time series with unknown statistics. Finally, the non-parametric and the GARCH based variations of our proposed integrated algorithm are applied on real YouTube video content views time series, to illustrate the performance of the proposed approach of volatility change detection.
ISSN: 2169-3536
Other Identifiers: 10.1109/ACCESS.2020.2972640
Appears in Collections:Department of Applied Informatics

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