  Increasing reliance on mobile broadband (MBB) networks for communication,
vehicle navigation, healthcare, and other critical purposes calls for improved
monitoring and troubleshooting of such networks. While recent advances in
monitoring with crowdsourced as well as network infrastructure-based methods
allow us to tap into a number of performance metrics from all layers of
networking, huge swaths of data remain poorly or completely unexplored due to a
lack of tools suitable for rapid, interactive, and rigorous MBB data analysis.
In this paper we present RICERCANDO, a MBB data mining toolkit developed in a
unique collaboration of networking and data mining experts. RICERCANDO consists
of a preprocessing module that ensures that time-series data is stored in the
most appropriate form for mining, a rapid exploration module that enables
iterative analysis of time-series and geomobile data, so that anomalies are
detected and singled out, and the advanced mining module that lets the analyst
deduce root causes of observed anomalies. We implement and release RICERCANDO
as open-source software, and validate its usability on case studies from MONROE
pan-European MBB measurement testbed.
