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Online event recognition from moving vessel trajectories

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Abstract

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.

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Notes

  1. http://www.imo.org/OurWork/Safety/Navigation/Pages/AIS.aspx

  2. https://www.vesselfinder.com

  3. http://www.aminess.eu/

  4. Typically for trajectories [7], linear interpolation is applied between each pair of successive measurements (p i ,τ i ) and (p i+1,τ i+1). For simplicity, we assume that this also holds in the case of vessels. With the exception of intermittent signals, their course between any two consecutive positions practically evolves in a very small area, which can be locally approximated with a Euclidean plane using Haversine distances.

  5. http://www.espertech.com/esper/

  6. http://sase.cs.umass.edu/

  7. Source code is publicly available at http://www.dblab.ece.ntua.gr/~kpatro/tools/streamAIS/.

  8. https://github.com/aartikis/RTEC.

  9. The patterns of the complex maritime events are available at http://users.iit.demokritos.gr/~a.artikis/aminess.tar.gz.

  10. This anonymized dataset (for privacy, each original MMSI has been replaced by a sequence number) is publicly available at http://chorochronos.datastories.org/?q=content/imis-3months

References

  1. Agrawal J, Diao Y, Gyllstrom D, Immerman N (2008) Efficient pattern matching over event streams. In: SIGMOD

  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 data

  3. Arasu A, Babu S, Widom J (2006) The CQL continuous query language: semantic foundations and query execution. VLDB J 15(2):121–142

    Article  Google Scholar 

  4. Artikis A, Sergot MJ, Paliouras G (2015) An event calculus for event recognition. IEEE Trans Knowl Data Eng 27(4):895–908

    Article  Google Scholar 

  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 337–346

  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 1100–1102

  7. Cao H, Wolfson O, Trajcevski G (2006) Spatio-temporal data reduction with deterministic error bounds. VLDB J 15(3):211–228

    Article  Google Scholar 

  8. Clark K (1978) Negation as failure. In: gallaire H., Minker J. (eds) Logic and Databases, pp. 293–322. Plenum Press

  9. Cugola G, Margara A (2010) TESLA: a formally defined event specification language. In: DEBS, pp 50–61

  10. Project datACRON Deliverable D5.1: Maritime use case and scenarios. http://ai-group.ds.unipi.gr/datacron/system/files/datACRON_D5.1.Maritime_Use_Case.pdf

  11. Dindar N, Fischer PM, Soner M, Tatbul N (2011) Efficiently correlating complex events over live and archived data streams. In: DEBS, pp 243–254

  12. Dousson C, Maigat PL (2007) Chronicle recognition improvement using temporal focusing and hierarchisation. In: IJCAI, pp 324–329

  13. Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159

    Google Scholar 

  14. Eckert M, Bry F (2010) Rule-based composite event queries: the language xchangeeq and its semantics. Knowl Inf Syst 25(3):551–573

    Article  Google Scholar 

  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 226–231

  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 1–8

  17. Golab L, Johnson T (2013) Data stream warehousing (tutorial). In: ACM SIGMOD, pp 949–952

  18. Idiri B, Napoli A (2012) The automatic identification system of maritime accident risk using rule-based reasoning. In: SoSE, pp 125–130

  19. Katsilieris F, Braca P, Coraluppi S (2013) Detection of Malicious AIS position spoofing by exploiting radar information. In: FUSION, pp 1196–1203

  20. Katzouris N, Artikis A, Paliouras G (2015) Incremental learning of event definitions with inductive logic programming. Mach Learn 100(2–3):555–585

    Article  Google Scholar 

  21. Kazemitabar SJ, Demiryurek U, Ali MH, Akdogan A, Shahabi C (2010) Geospatial stream query processing using Microsoft SQL Server Streaminsight. PVLDB 3(2):1537–1540

    Google Scholar 

  22. Kowalski R, Sergot M (1986) A logic-based calculus of events New Generation Computing 4(1)

  23. Krämer J, Seeger B (2009) Semantics and implementation of continuous sliding window queries over data streams ACM Transactions on Database Systems 34(1)

  24. van Laere J, Nilsson M (2009) Evaluation of a workshop to capture knowledge from subject matter experts in maritime surveillance. In: FUSION, pp 171–178

  25. Lange R, Dürr F, Rothermel K (2011) Efficient real-time trajectory tracking. VLDB J 20(5):671–694

    Article  Google Scholar 

  26. Li G, Jacobsen HA (2005) Composite subscriptions in content-based publish/subscribe systems. In: Middleware

  27. Meratnia N, de By R (2004) Spatiotemporal compression techniques for moving point objects. In: EDBT, pp 765–782

  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 359–365

  29. Moga A, Tatbul N (2011) UpStream: A storage-centric load management system for real-time update streams. PVLDB 4(12):1442–1445

    Google Scholar 

  30. O’Rourke J (1998) Computational Geometry in C cambridge university press

  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):2218–2245

    Article  Google Scholar 

  32. Paschke A, Kozlenkov A (2009) Rule-based event processing and reaction rules. In: RuleML, LNCS 5858

  33. Patroumpas K, Artikis A, Katzouris N, Vodas M, Theodoridis Y, Pelekis N (2015) Event recognition for maritime surveillance. In: EDBT, pp 629–640

  34. Patroumpas K, Sellis T (2011) Maintaining consistent results of continuous queries under diverse window specifications. Inf Syst 36(1):42–61

    Article  Google Scholar 

  35. Potamias M, Patroumpas K, Sellis T (2007) Online amnesic summarization of streaming locations. In: SSTD, pp 148–165

  36. Przymusinski T (1987) On the declarative semantics of stratified deductive databases and logic programs. In: Found. of deductive databases and logic programming. Morgan

  37. Shahir HY, Glasser U, Shahir AY, Wehn H (2015) Maritime situation analysis framework: Vessel interaction classification and anomaly detection. In: Big Data, pp 1279–1289

  38. Skarlatidis A, Paliouras G, Artikis A, Vouros G (2015) Probabilistic event calculus for event recognition ACM Transactions on Computational Logic 16(2)

  39. Snidaro L, Visentini I, Bryan K (2015) Fusing uncertain knowledge and evidence for maritime situational awareness via markov logic networks. Inf Fusion 21:159–172

    Article  Google Scholar 

  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 1–16

  41. Wolfson O, Sistla A, Chamberlain S, Yesha Y (1999) Updating and querying databases that track mobile units. Distributed & Parallel Databases 7(3):257–287

    Article  Google Scholar 

  42. Zhang H, Diao Y, Immerman N (2014) On complexity and optimization of expensive queries in complex event processing. In: SIGMOD, pp 217–228

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Acknowledgments

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.

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Authors and Affiliations

  1. School of Electrical, Computer Engineering, National Technical University of Athens, Athens, Greece

    Kostas Patroumpas

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Kostas Patroumpas, Marios Vodas & Yannis Theodoridis

  3. Institute of Informatics, Telecommunications, NCSR Demokritos, Athens, Greece

    Elias Alevizos & Alexander Artikis

  4. Department of Maritime Studies, University of Piraeus, Piraeus, Greece

    Alexander Artikis

  5. Department of Statistics, Insurance Science, University of Piraeus, Piraeus, Greece

    Nikos Pelekis

Authors
  1. Kostas Patroumpas
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  2. Elias Alevizos
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  3. Alexander Artikis
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  4. Marios Vodas
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  5. Nikos Pelekis
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  6. Yannis Theodoridis
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Correspondence to Elias Alevizos.

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Patroumpas, K., Alevizos, E., Artikis, A. et al. Online event recognition from moving vessel trajectories. Geoinformatica 21, 389–427 (2017). https://doi.org/10.1007/s10707-016-0266-x

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  • DOI: https://doi.org/10.1007/s10707-016-0266-x

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