Labelling relevant events to support the crisis management operator

Thanks to the large availability of portable devices and the growing interest in the Internet of Things, during crises, social networks, or alerts sent through mobile devices or sensor networks are available and can be matched each other to perform situational analysis. However, the inclusion of multiple heterogeneous sources in situational analyses leads to 2 main issues: (1) a source could deliver (voluntarily or erroneously) wrong data damaging the integrity and the correctness of the analysis, and (2) a significant amount of heterogeneous data need to be processed. As a consequence, the crisis management operator faces a large amount of potentially unreliable data. In this paper, we present a relevance labelling strategy to process information gathered from heterogeneous data streams to select the most relevant events. These are presented to the crisis management operator with the highest priority. Our strategy is evaluated using events collected by the Secure! crisis management system, considering 3 real crisis scenarios happened in Italy in 2015. Results show that our strategy is able to correctly identify sets of relevant events, supporting the activities of the crisis management operator.