The discovery of anomalies and, more in general, of events of interest at sea is one of the main challenges of Maritime Situational Awareness (MSA)-related activities. This paper proposes an event-based methodology for knowledge discovery without querying directly a huge amount 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 maritime data, without accessing the raw AIS data, 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 illustrated and assessed on real-world AIS data set revealing computational improvements and enriched monitoring capabilities.

ALVAREZ ALVAREZ Marlene;  FERNANDEZ ARGUEDAS Virginia;  GAMMIERI Vincenzo;  VESPE Michele;  AULICINO Giuseppe;  VOLLERO Antonio; 

MAZZARELLA Fabio; 

2016-09-16

IEEE

JRC100883

978-0-9964-5274-8,   

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7528111&refinements%3D4224302807%26filter%3DAND%28p_IS_Number%3A7527857%29,    https://publications.jrc.ec.europa.eu/repository/handle/JRC100883,