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Our paper “Complex Event Recognition in the Big Data Era” was accepted at VLDB Journal


Georgios M. Santipantakis, Akrivi Vlachou, Christos Doulkeridis, Alexander Artikis, Ioannis Kontopoulos and George A. Vouros


We present a stream reasoning system for monitoring vessel activity in large geographical areas. The system ingests a compressed vessel position stream, and performs online spatio-temporal discovery to calculate proximity relations between vessels, and topological relations between vessel and static areas. Capitalizing on the discovered relations, a complex activity recognition engine, based on the Event Calculus, performs continuous pattern matching to detect various types of dangerous, suspicious and potentially illegal vessel activity. We evaluate the performance of the system by means of real datasets including kinematic messages from vessels, and demonstrate the effects of the highly efficient spatio-temporal link discovery on performance.

Complex Event Recognition in the Big Data Era