Labelling Relevant Events to Support the Crisis Management Operator

Research Area: Uncategorized Year: 2017
Type of Publication: Article Keywords: Crisis Management System; Human Sensors; Heterogeneous Data; Data Filtering; Relevance Labelling; Twitter
Authors: Tommaso Zoppi; Andrea Ceccarelli; Francesco lo Piccolo; Paolo Lollini; Gabriele Giunta; Vito Morreale; Andrea Bondavalli
Journal: Journal of Software: Evolution and Process
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 net-works are available and can be matched each other to perform situational analysis. However, the inclusion of multiple heterogeneous sources in situational analysis leads to two main is-sues: i) a source could deliver (voluntarily or erroneously) wrong data damaging the integrity and the correctness of the analysis, and ii) 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 three 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.

Resilient Computing Lab, 2011

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