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Entropy 2017, 19(2), 74;

An Approach to Data Analysis in 5G Networks

Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, Madrid 28040, Spain
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 16 January 2017 / Revised: 13 February 2017 / Accepted: 14 February 2017 / Published: 16 February 2017
(This article belongs to the Special Issue Information Theory and 5G Technologies)
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5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness. View Full-Text
Keywords: 5G; data analysis; network function virtualization; situational awareness; software defined networking 5G; data analysis; network function virtualization; situational awareness; software defined networking

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Barona López, L.I.; Maestre Vidal, J.; García Villalba, L.J. An Approach to Data Analysis in 5G Networks. Entropy 2017, 19, 74.

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