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Complex Event Recognition for Maritime Monitoring

According to the International Maritime Organization (IMO), 90% of the global trade is handled by the shipping industry. Maritime monitoring systems support safe shipping by detecting, in real-time, dangerous, suspicious and illegal vessel activities. Such systems typically use the Automatic Identification System (AIS), a tracking technology for locating vessels at sea through data exchange. We have been developing a complex event recognition system for maritime monitoring in the Event Calculus that uses AIS as its main source of information, along with contextual geographical information (e.g, Fishing areas, Natura 2000 areas, depth information etc.).

Recognition methods

We perform complex event recognition using the ‘Event Calculus for Run-Time reasoning’ (RTEC), an open-source Prolog implementation of the Event Calculus, designed to compute continuous temporal projection queries for pattern matching on data streams.

Detected maritime activities

We detect:

  • anchored vessels,
  • drifting vessels (when true heading deviates from course over ground),
  • tugging operations,
  • piloting,
  • trawling,
  • speed violations,
  • vessel rendez-vous,
  • loitering, and
  • search and rescue operations.

See the videos below for some examples (best viewed in full screen mode).


The detected instances of all composite maritime activities mentioned above, over a period of 6 months (October 2015-March 2016), in the area of Brest, France, is available here. The AIS signals of the dataset are available here, while a semantic annotation of the dataset is available here.


Pitsikalis M., Artikis A., Dreo R., Ray C., Camossi E., and Jousselme A. Composite Event Recognition for Maritime Monitoring. International Conference on Distributed and Event-Based Systems (DEBS), 2019 (pdf) (slides) (bibtex)

Tsilionis E., Artikis A. and Paliouras G. Incremental Event Calculus for Run-Time Reasoning. International Conference on Distributed and Event-Based Systems (DEBS), 2019. (pdf) (slides) (bibtex)

Katzouris N., Artikis A. and Paliouras G., Parallel Online Event Calculus Learning for Complex Event Recognition, Future Generation Computer Systems, Vol. 94, pp. 468-478, 2019. (pdf)

Michelioudakis E., Artikis A. and Paliouras G., Semi-Supervised Online Structure Learning for Composite Event Recognition, Machine Learning Journal, to appear. (pdf)

Santipantakis G., Vlachou A., Doulkeridis C., Artikis A., Kontopoulos I. and Vouros G. A Stream Reasoning System for Maritime Monitoring. International Symposium on Temporal Representation and Reasoning (TIME) 2018. (pdf)

Alevizos E., Artikis A. and Paliouras G.: Event Forecasting with Pattern Markov Chains, Proceedings of Distributed and Event-Based Systems (DEBS) 2017. (pdf)

Patroumpas K., Alevizos E., Artikis A., Vodas M., Pelekis N., Theodoridis Y.: Online event recognition from moving vessel trajectories. GeoInformatica 21(2): 389-427 (2017) (pdf)

Artikis A., Sergot M. and Paliouras G. An Event Calculus for Event Recognition. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(4): 895-908. (pdf)