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Interpretable Learning In Multivariate Big Data Analysis For Network Monitoring, José Camacho, Rasmus Bro, David Kotz
Interpretable Learning In Multivariate Big Data Analysis For Network Monitoring, José Camacho, Rasmus Bro, David Kotz
Dartmouth Scholarship
There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks. For many applications, like network monitoring and troubleshooting, a data model is of little use if it cannot be interpreted by a human operator. In this paper, we present an extension of the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool. In this extension, we propose a solution to the automatic derivation of features, a cornerstone step for the application of MBDA when the amount of data is massive. The resulting network monitoring approach allows …