Abstract
How can movements of vessels at sea be recognized and be related to features of vessels? In this study we focus on data from AIS (Automatic Identification System) and we use historical AIS data as the basis for recognition and we extend this with prior knowledge in the form of an ontology. We present solutions for three problems. First we show that a form of piecewise linear segmentation allows strong compression of AIS data without much loss of important information. Second, we show that similarities between movements can well be measured using a form of edit-distance. These similarities are then used to predict properties of vessels and to discover clusters of vessel movement. Finally we show how knowledge that is represented in an ontology can be used in knowledge-intensive similarity which produces better results than using only movement data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Chen L, Ng R (2004) On the marriage of lp-norms and edit distance. In: VLDB ’04: proceedings of the thirtieth international conference on very large data bases. VLDB Endowment, Toronto, Canada, pp 792–803
de Vries GKD (2012) Kernel methods for vessel trajectories. Ph.D. thesis, University of Amsterdam. http://dare.uva.nl/document/357350
de Vries G, van Someren M (2010) Clustering vessel trajectories with alignment kernels under trajectory compression. In: Balcázar JL, Bonchi F, Gionis A, Sebag M (eds) European conference on machine learning and knowledge discovery in databases (ECML PKDD 2010). Lecture notes in computer science, vol 6321. Springer, Dallas, TX pp 296–311
Douglas DH, Peucker TK (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica 10(2):112–122
Gudmundsson J, Katajainen J, Merrick D, Ong C, Wolle T (2009) Compressing spatio-temporal trajectories. Comput Geom 42(9):825–841
Schölkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge
Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, New York
Vries GKD, van Hage WR, van Someren M (2010) Comparing vessel trajectories using geographical domain knowledge and alignments. In: Fan W, Hsu W, Webb GI, Liu B, Zhang C, Gunopulos D, Wu X (eds) The 10th IEEE international conference on data mining workshops (ICDMW 2010). IEEE Computer Society, Los Alamitos, pp 209–216
Willems N, van Hage WR, de Vries G, Janssens JHM, Malaisé V (2010) An integrated approach for visual analysis of a multisource moving objects knowledge base. Int J Geogr Inf Sci 24(10):1543–1558
Acknowledgements
This research has been carried out as a part of the Poseidon project at Thales under the responsibilities of the Embedded Systems Institute (ESI). This project is partially supported by the Dutch Ministry of Economic Affairs under the BSIK program.
The authors wish to thank Willem van Hage and Véronique Malaisé for providing the geographical ontology, and Pierre van de Laar, Jan Tretmans, Richard Doornbos and Wil van Dommelen for their comments on a earlier version of this chapter.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Vries, G.d., van Someren, M. (2013). Recognizing Vessel Movements from Historical Data. In: van de Laar, P., Tretmans, J., Borth, M. (eds) Situation Awareness with Systems of Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6230-9_7
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6230-9_7
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6229-3
Online ISBN: 978-1-4614-6230-9
eBook Packages: EngineeringEngineering (R0)