, Volume 21, Issue 2, pp 389–427 | Cite as

Online event recognition from moving vessel trajectories

  • Kostas Patroumpas
  • Elias AlevizosEmail author
  • Alexander Artikis
  • Marios Vodas
  • Nikos Pelekis
  • Yannis Theodoridis


We present a system for online monitoring of maritime activity over streaming positions from numerous vessels sailing at sea. The system employs an online tracking module for detecting important changes in the evolving trajectory of each vessel across time, and thus can incrementally retain concise, yet reliable summaries of its recent movement. In addition, thanks to its complex event recognition module, this system can also offer instant notification to marine authorities regarding emergency situations, such as suspicious moves in protected zones, or package picking at open sea. Not only did our extensive tests validate the performance, efficiency, and robustness of the system against scalable volumes of real-world and synthetically enlarged datasets, but its deployment against online feeds from vessels has also confirmed its capabilities for effective, real-time maritime surveillance.


AIS Event recognition Geostreaming Moving objects Trajectorys 



This work was funded partly by the “AMINESS: Analysis of Marine INformation for Environmentally Safe Shipping” project, which was co-financed by the European Fund for Regional Development and from Greek National funds, and partly by the EU-funded H2020 datACRON project (H2020-ICT-2015 687591). We wish to thank IMIS Hellas, our partner in AMINESS, for providing the AIS dataset used in the experiments.


  1. 1.
    Agrawal J, Diao Y, Gyllstrom D, Immerman N (2008) Efficient pattern matching over event streams. In: SIGMODGoogle Scholar
  2. 2.
    Alevizos E, Artikis A, Patroumpas K, Vodas M, Theodoridis Y, Pelekis N (2015) How not to drown in a sea of information: an event recognition approach. In: IEEE International conference on big dataGoogle Scholar
  3. 3.
    Arasu A, Babu S, Widom J (2006) The CQL continuous query language: semantic foundations and query execution. VLDB J 15(2):121–142CrossRefGoogle Scholar
  4. 4.
    Artikis A, Sergot MJ, Paliouras G (2015) An event calculus for event recognition. IEEE Trans Knowl Data Eng 27(4):895–908CrossRefGoogle Scholar
  5. 5.
    Bai Y, Thakkar H, Wang H, Luo C, Zaniolo C (2006) A data stream language and system designed for power and extensibility. In: CIKM, pp 337–346Google Scholar
  6. 6.
    Brenna L, Demers AJ, Gehrke J, Hong M, Ossher J, Panda B, Riedewald M, Thatte M, White WM (2007) Cayuga: a high-performance event processing engine. In: SIGMOD, pp 1100–1102Google Scholar
  7. 7.
    Cao H, Wolfson O, Trajcevski G (2006) Spatio-temporal data reduction with deterministic error bounds. VLDB J 15(3):211–228CrossRefGoogle Scholar
  8. 8.
    Clark K (1978) Negation as failure. In: gallaire H., Minker J. (eds) Logic and Databases, pp. 293–322. Plenum PressGoogle Scholar
  9. 9.
    Cugola G, Margara A (2010) TESLA: a formally defined event specification language. In: DEBS, pp 50–61Google Scholar
  10. 10.
    Project datACRON Deliverable D5.1: Maritime use case and scenarios.
  11. 11.
    Dindar N, Fischer PM, Soner M, Tatbul N (2011) Efficiently correlating complex events over live and archived data streams. In: DEBS, pp 243–254Google Scholar
  12. 12.
    Dousson C, Maigat PL (2007) Chronicle recognition improvement using temporal focusing and hierarchisation. In: IJCAI, pp 324–329Google Scholar
  13. 13.
    Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159Google Scholar
  14. 14.
    Eckert M, Bry F (2010) Rule-based composite event queries: the language xchangeeq and its semantics. Knowl Inf Syst 25(3):551–573CrossRefGoogle Scholar
  15. 15.
    Ester M, Kriegel H, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp 226–231Google Scholar
  16. 16.
    Garcia J, Gomez-Romero J, Patricio M, Molina J, Rogova G (2011) On the representation and exploitation of context knowledge in a harbor surveillance scenario. In: FUSION, pp 1–8Google Scholar
  17. 17.
    Golab L, Johnson T (2013) Data stream warehousing (tutorial). In: ACM SIGMOD, pp 949–952Google Scholar
  18. 18.
    Idiri B, Napoli A (2012) The automatic identification system of maritime accident risk using rule-based reasoning. In: SoSE, pp 125–130Google Scholar
  19. 19.
    Katsilieris F, Braca P, Coraluppi S (2013) Detection of Malicious AIS position spoofing by exploiting radar information. In: FUSION, pp 1196–1203Google Scholar
  20. 20.
    Katzouris N, Artikis A, Paliouras G (2015) Incremental learning of event definitions with inductive logic programming. Mach Learn 100(2–3):555–585CrossRefGoogle Scholar
  21. 21.
    Kazemitabar SJ, Demiryurek U, Ali MH, Akdogan A, Shahabi C (2010) Geospatial stream query processing using Microsoft SQL Server Streaminsight. PVLDB 3(2):1537–1540Google Scholar
  22. 22.
    Kowalski R, Sergot M (1986) A logic-based calculus of events New Generation Computing 4(1)Google Scholar
  23. 23.
    Krämer J, Seeger B (2009) Semantics and implementation of continuous sliding window queries over data streams ACM Transactions on Database Systems 34(1)Google Scholar
  24. 24.
    van Laere J, Nilsson M (2009) Evaluation of a workshop to capture knowledge from subject matter experts in maritime surveillance. In: FUSION, pp 171–178Google Scholar
  25. 25.
    Lange R, Dürr F, Rothermel K (2011) Efficient real-time trajectory tracking. VLDB J 20(5):671–694CrossRefGoogle Scholar
  26. 26.
    Li G, Jacobsen HA (2005) Composite subscriptions in content-based publish/subscribe systems. In: MiddlewareGoogle Scholar
  27. 27.
    Meratnia N, de By R (2004) Spatiotemporal compression techniques for moving point objects. In: EDBT, pp 765–782Google Scholar
  28. 28.
    Millefiori LM, Braca P, Bryan K, Willett P (2015) Adaptive filtering of imprecisely time-stamped measurements with application to AIS networks. In: FUSION, pp 359–365Google Scholar
  29. 29.
    Moga A, Tatbul N (2011) UpStream: A storage-centric load management system for real-time update streams. PVLDB 4(12):1442–1445Google Scholar
  30. 30.
    O’Rourke J (1998) Computational Geometry in C cambridge university pressGoogle Scholar
  31. 31.
    Pallotta G, Vespe M, Bryan K (2013) Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy 15 (6):2218–2245CrossRefGoogle Scholar
  32. 32.
    Paschke A, Kozlenkov A (2009) Rule-based event processing and reaction rules. In: RuleML, LNCS 5858Google Scholar
  33. 33.
    Patroumpas K, Artikis A, Katzouris N, Vodas M, Theodoridis Y, Pelekis N (2015) Event recognition for maritime surveillance. In: EDBT, pp 629–640Google Scholar
  34. 34.
    Patroumpas K, Sellis T (2011) Maintaining consistent results of continuous queries under diverse window specifications. Inf Syst 36(1):42–61CrossRefGoogle Scholar
  35. 35.
    Potamias M, Patroumpas K, Sellis T (2007) Online amnesic summarization of streaming locations. In: SSTD, pp 148–165Google Scholar
  36. 36.
    Przymusinski T (1987) On the declarative semantics of stratified deductive databases and logic programs. In: Found. of deductive databases and logic programming. MorganGoogle Scholar
  37. 37.
    Shahir HY, Glasser U, Shahir AY, Wehn H (2015) Maritime situation analysis framework: Vessel interaction classification and anomaly detection. In: Big Data, pp 1279–1289Google Scholar
  38. 38.
    Skarlatidis A, Paliouras G, Artikis A, Vouros G (2015) Probabilistic event calculus for event recognition ACM Transactions on Computational Logic 16(2)Google Scholar
  39. 39.
    Snidaro L, Visentini I, Bryan K (2015) Fusing uncertain knowledge and evidence for maritime situational awareness via markov logic networks. Inf Fusion 21:159–172CrossRefGoogle Scholar
  40. 40.
    Terroso-Saenz F, Valdes-Vela M, Skarmeta-Gomez AF (2015) A complex event processing approach to detect abnormal behaviours in the marine environment. Information Systems Frontiers 1–16Google Scholar
  41. 41.
    Wolfson O, Sistla A, Chamberlain S, Yesha Y (1999) Updating and querying databases that track mobile units. Distributed & Parallel Databases 7(3):257–287CrossRefGoogle Scholar
  42. 42.
    Zhang H, Diao Y, Immerman N (2014) On complexity and optimization of expensive queries in complex event processing. In: SIGMOD, pp 217–228Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Kostas Patroumpas
    • 1
    • 2
  • Elias Alevizos
    • 3
    Email author
  • Alexander Artikis
    • 3
    • 4
  • Marios Vodas
    • 2
  • Nikos Pelekis
    • 5
  • Yannis Theodoridis
    • 2
  1. 1.School of Electrical, Computer EngineeringNational Technical University of AthensAthensGreece
  2. 2.Department of InformaticsUniversity of PiraeusPiraeusGreece
  3. 3.Institute of Informatics, TelecommunicationsNCSR DemokritosAthensGreece
  4. 4.Department of Maritime StudiesUniversity of PiraeusPiraeusGreece
  5. 5.Department of Statistics, Insurance ScienceUniversity of PiraeusPiraeusGreece

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