  In the never-ending quest for tools that enable an ISP to smooth
troubleshooting and improve awareness of network behavior, very much effort has
been devoted in the collection of data by active and passive measurement at the
data plane and at the control plane level. Exploitation of collected data has
been mostly focused on anomaly detection and on root-cause analysis. Our
objective is somewhat in the middle. We consider traceroutes collected by a
network of probes and aim at introducing a practically applicable methodology
to quickly spot measurements that are related to high-impact events happened in
the network. Such filtering process eases further in- depth human-based
analysis, for example with visual tools which are effective only when handling
a limited amount of data. We introduce the empathy relation between traceroutes
as the cornerstone of our formal characterization of the traceroutes related to
a network event. Based on this model, we describe an algorithm that finds
traceroutes related to high-impact events in an arbitrary set of measurements.
Evidence of the effectiveness of our approach is given by experimental results
produced on real-world data.
