Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring

arXiv:1907.02677v3 Announce Type: replace-cross
Abstract: 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 …

abstract analysis applications arxiv big big data big data analysis communication cs.lg cs.ni data data analysis data-driven data model development feature human interpreted monitoring multivariate network networks performance stat.ml troubleshooting type