The explosion in volume and heterogeneity of data communication network measurements opens the door to the massive applica- tion of machine learning and artificial intelligence technology in networking. While machine learning is today systematically and successfully applied in many other data-driven domains, its appli- cation is in an infancy stage of development in the networking domain. The ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big- DAMA, fosters the research and development of novel analytical approaches and technical solutions that can exploit Big Data tech- nology in the analysis of complex communication networks such as the Internet.

ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks / Casas, P.; Mellia, M.; Dainotti, A.; Zseby, T.. - ELETTRONICO. - 1:(2017), pp. 1-1. (Intervento presentato al convegno Big Data Analytics and Machine Learning for Data Communication Networks tenutosi a Los Angeles nel August 2017).

ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks

Mellia M.;
2017

Abstract

The explosion in volume and heterogeneity of data communication network measurements opens the door to the massive applica- tion of machine learning and artificial intelligence technology in networking. While machine learning is today systematically and successfully applied in many other data-driven domains, its appli- cation is in an infancy stage of development in the networking domain. The ACM SIGCOMM Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big- DAMA, fosters the research and development of novel analytical approaches and technical solutions that can exploit Big Data tech- nology in the analysis of complex communication networks such as the Internet.
2017
978-1-4503-5054-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2751335
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