RIASSUNTO
The discovery of anomalies and, more in general, of events of interest at sea is one of the main challenges of Maritime Situational Awareness. This paper introduces an event-based methodology for knowledge discovery without querying directly a large volume of raw data. The proposed architecture analyses the maritime traffic data to detect maritime traffic patterns and events and aggregate them in an Event Map, namely a georeferenced grid. The Event Map offers visualisation capabilities and, more importantly, is used as access interface to the maritime traffic knowledge database. The proposed methodology offers real-time access to the extracted maritime knowledge, and the possibility to perform more structured queries with respect to traditional basic queries (e. g. vessel proximity within a certain distance). The proposed approach is demonstrated and assessed using real-world Automatic Identification System (AIS) data, revealing computational improvements and enriched monitoring capabilities.