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.
Similar content being viewed by others
Notes
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.
Source code is publicly available at http://www.dblab.ece.ntua.gr/~kpatro/tools/streamAIS/.
The patterns of the complex maritime events are available at http://users.iit.demokritos.gr/~a.artikis/aminess.tar.gz.
This anonymized dataset (for privacy, each original MMSI has been replaced by a sequence number) is publicly available at http://chorochronos.datastories.org/?q=content/imis-3months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
Agrawal J, Diao Y, Gyllstrom D, Immerman N (2008) Efficient pattern matching over event streams. In: SIGMOD
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
Arasu A, Babu S, Widom J (2006) The CQL continuous query language: semantic foundations and query execution. VLDB J 15(2):121–142
Artikis A, Sergot MJ, Paliouras G (2015) An event calculus for event recognition. IEEE Trans Knowl Data Eng 27(4):895–908
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
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
Cao H, Wolfson O, Trajcevski G (2006) Spatio-temporal data reduction with deterministic error bounds. VLDB J 15(3):211–228
Clark K (1978) Negation as failure. In: gallaire H., Minker J. (eds) Logic and Databases, pp. 293–322. Plenum Press
Cugola G, Margara A (2010) TESLA: a formally defined event specification language. In: DEBS, pp 50–61
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
Dindar N, Fischer PM, Soner M, Tatbul N (2011) Efficiently correlating complex events over live and archived data streams. In: DEBS, pp 243–254
Dousson C, Maigat PL (2007) Chronicle recognition improvement using temporal focusing and hierarchisation. In: IJCAI, pp 324–329
Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159
Eckert M, Bry F (2010) Rule-based composite event queries: the language xchangeeq and its semantics. Knowl Inf Syst 25(3):551–573
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
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
Golab L, Johnson T (2013) Data stream warehousing (tutorial). In: ACM SIGMOD, pp 949–952
Idiri B, Napoli A (2012) The automatic identification system of maritime accident risk using rule-based reasoning. In: SoSE, pp 125–130
Katsilieris F, Braca P, Coraluppi S (2013) Detection of Malicious AIS position spoofing by exploiting radar information. In: FUSION, pp 1196–1203
Katzouris N, Artikis A, Paliouras G (2015) Incremental learning of event definitions with inductive logic programming. Mach Learn 100(2–3):555–585
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
Kowalski R, Sergot M (1986) A logic-based calculus of events New Generation Computing 4(1)
Krämer J, Seeger B (2009) Semantics and implementation of continuous sliding window queries over data streams ACM Transactions on Database Systems 34(1)
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
Lange R, Dürr F, Rothermel K (2011) Efficient real-time trajectory tracking. VLDB J 20(5):671–694
Li G, Jacobsen HA (2005) Composite subscriptions in content-based publish/subscribe systems. In: Middleware
Meratnia N, de By R (2004) Spatiotemporal compression techniques for moving point objects. In: EDBT, pp 765–782
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
Moga A, Tatbul N (2011) UpStream: A storage-centric load management system for real-time update streams. PVLDB 4(12):1442–1445
O’Rourke J (1998) Computational Geometry in C cambridge university press
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
Paschke A, Kozlenkov A (2009) Rule-based event processing and reaction rules. In: RuleML, LNCS 5858
Patroumpas K, Artikis A, Katzouris N, Vodas M, Theodoridis Y, Pelekis N (2015) Event recognition for maritime surveillance. In: EDBT, pp 629–640
Patroumpas K, Sellis T (2011) Maintaining consistent results of continuous queries under diverse window specifications. Inf Syst 36(1):42–61
Potamias M, Patroumpas K, Sellis T (2007) Online amnesic summarization of streaming locations. In: SSTD, pp 148–165
Przymusinski T (1987) On the declarative semantics of stratified deductive databases and logic programs. In: Found. of deductive databases and logic programming. Morgan
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
Skarlatidis A, Paliouras G, Artikis A, Vouros G (2015) Probabilistic event calculus for event recognition ACM Transactions on Computational Logic 16(2)
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
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
Wolfson O, Sistla A, Chamberlain S, Yesha Y (1999) Updating and querying databases that track mobile units. Distributed & Parallel Databases 7(3):257–287
Zhang H, Diao Y, Immerman N (2014) On complexity and optimization of expensive queries in complex event processing. In: SIGMOD, pp 217–228
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10707-016-0266-x