  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 us to detect and diagnose disparate network
anomalies, with a data-analysis workflow that combines the advantages of
interpretable and interactive models with the power of parallel processing. We
apply the extended MBDA to two case studies: UGR'16, a benchmark flow-based
real-traffic dataset for anomaly detection, and Dartmouth'18, the longest and
largest Wi-Fi trace known to date.
