In this paper, we discuss a software architecture, which has been developed for the needs of the System for Intelligent Maritime Monitoring (SIMMO). The system bases on the state-of-the-art information fusion and intelligence analysis techniques, which generates an enhanced Recognized Maritime Picture and thus supports situation analysis and decision- making. The SIMMO system aims to automatically fuse an up-to-date maritime data from Automatic Identification System (AIS) and open Internet sources. Based on collected data, data analysis is performed to detect suspicious vessels. Functionality of the system is realized in a number of different modules (web crawlers, data fusion, anomaly detection, visualization modules) that share the AIS and external data stored in the system’s database. The aim of this article is to demonstrate how external information can be leveraged in maritime awareness system and what software solutions are necessary. A working system is presented as a proof of concept.
 Bouejla A., Chaze X., Guarnieri F., Napoli A., A Bayesian network to manage risks of maritime piracy against o shore oil fields, ‘Safety Science’, 2014, Vol. 68, pp. 222-230.
 Brax C., Visual Analytics for Maritime Anomaly Detection. PhD thesis, Orebro University, ‘School of Science and Technology’, 2011.
 Chaze X., Bouejla A., Napoli A., Guarnieri F., Eude T., Alhadef B., The contribution of Bayesian networks to manage risks of maritime piracy against oil offshore fields, Database Systems for Advanced Applications, LNCS, 7240, Springer-Berlin Heidelberg, 2012, pp. 81-91.
 Chen C. H., Khoo L. P., Chong Y. T., Yin X. F., Knowledge discovery using genetic algorithm for maritime situational awareness, ‘Expert Syst. Appl.’, 2014, Vol. 41, Issue 6, pp. 2742-2753.
 Felski A., Jaskólski K., Banyś P., Comprehensive Assesment of Automatic Identification System (AIS) Data Application to Anti-collision Manoeuvring, ‘The Journal of Navigation’, 2015, Vol. 68, pp. 697-717.
 Fischer Y., Bauer A., Object-oriented sensor data fusion for wide maritime surveillance, Waterside Security Conference (WSS), 2010, pp. 1-6.
 Giraud M. A., Alhadef B., Guarnieri F., Napoli A., Bottala-Gambetta M., et al., SARGOS: Securing Offshore Infrastructures Through a Global Alert and Graded Response System, Maritime System and Technology, 2011, Marseille, France.
 Hevner A., March S. T., Park J., Ram S., Design science in information systems research, ‘MIS Quartely’, 2004, Vol. 28, No. 1, pp. 75-105.
 International Maritime Organization, International shipping facts and figures information resources on trade, safety, security, environment (technical report), 2012.
 International Telecommunication Union, Recommendation ITU-T M.1371-4. Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band, 2010.
 Johansson F., Falkman G., Detection of vessel anomalies - a Bayesian network approach, 3rd Int. Conf. on Intelligent Sensors, Sensor Networks and Information, IEEE, 2007.
 Kazemi S., Abghari S., Lavesson N., Johnson H., Ryman P., Open data for anomaly detection in maritime surveillance, ‘Expert Syst. Appl.’, 2013, Vol. 40, Issue 14, pp. 5719-5729.
 Pallotta G., Vespe M., Bryan K., Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction, ‘Entropy’, 2013, Vol. 15, Issue 6, pp. 2218-2245.
 Riveiro M., Falkman G., Ziemke T., Improving maritime anomaly detection and situation awareness through interactive visualization, Information Fusion, 11th International Conference on, IEEE, 2008, pp. 1-8.
 Vespe M., Sciotti M., Battistello G., Multi-sensor autonomous tracking for maritime surveillance, International Conference on Radar, IEEE, 2008, pp. 525-530.