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dc.contributor.authorDEVOGELE, Thomas
dc.contributor.author
 hal.structure.identifier
ETIENNE, Laurent
32440 Dalhousie University [Halifax]
dc.contributor.author
 hal.structure.identifier
RAY, Cyril
13094 Institut de Recherche de l'Ecole Navale [IRENAV]
dc.date.accessioned2015
dc.date.available2015
dc.date.issued2013
dc.date.submitted2015
dc.identifier.isbn9781107021716
dc.identifier.urihttp://hdl.handle.net/10985/9702
dc.description.abstractThe maritime environment still represents an unexploited potential for modelling, management and understanding of mobility data. The environment is diverse, open but partly ruled, and covers a large spectrum of ships from small sailing-boats to super tankers which generally exhibit type-related behaviours. Similarly to terrestrial or aerial domains, several real-time positioning systems, such as the Automatic Identification System (AIS), have been developed for keeping track of vessel movements. However the huge amounts of data provided by these reporting systems are rarely used for knowledge discovery. This chapter aims at discussing different aspects of maritime mobilities understanding. This chapter enables readers to, first, understand the intrinsic behaviour of maritime positioning systems and then proposes a methodology to illustrate the different steps leading to trajectory patterns for the understanding of outlier detection.
dc.language.isoen
dc.publisherCambridge University Press
dc.rightsPost-print
dc.subjectMobility Data
dc.subjectSpatio-Temporal Pattern Mining
dc.subjectAIS
dc.titleMaritime monitoring
dc.typdocChapitre d'ouvrage scientifique
dc.localisationCentre de Paris
dc.subject.halInformatique: Base de données
ensam.title.proceedingMobility Data: Modelling, Management, and Understanding
ensam.page224-243
hal.identifierhal-01170999
hal.version1
hal.statusaccept


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