Current students

The ability to track large-scale events as they happen is essential for understanding them and coordinating reactions in an appropriate and timely manner. This is true for emergency management and decision-making support, where quality and latency constraints are stringent. In some contexts, real-time and large-scale sensor data and forecasts may be available. The hypothesis to be explored is whether ‘classic’ data sources can be augmented with the ingestion of semi-structured data sources, such as social media. Social media can diffuse valuable knowledge, such as direct witness or expert opinions, while their noisy nature makes them not trivial to manage, calling for specific monitoring and mining tools. The derived knowledge could be used to complement and confirm spatio-temporal descriptions of events, highlighting previously unseen or undervalued aspects. The critical functionalities, such as event sensing, multilingualism, selection of visual evidence, geolocation, data processing, and data fusion will be investigated, as a foundation for a unified spatio-temporal representation of multi-modal event characterizations.