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Maritime Data Processing in Relational Databases

hal.structure.identifierKnowledge Learning and Information Modelling [LABISEN-KLAIM]
dc.contributor.authorETIENNE, Laurent
hal.structure.identifierInstitut de Recherche de l'Ecole Navale [IRENAV]
dc.contributor.authorRAY, Cyril
hal.structure.identifierCentre for Maritime Research and Experimentation - Science and Technology Organisation [CMRE - STO]
dc.contributor.authorCAMOSSI, Elena
hal.structure.identifierCentre for Maritime Research and Experimentation - Science and Technology Organisation [CMRE - STO]
dc.contributor.authorIPHAR, Clément
dc.date.accessioned2021-05-14T09:31:15Z
dc.date.available2021-05-14T09:31:15Z
dc.date.issued2021-02-09
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/75873
dc.description.abstractEnMaritime data processing research has long used spatio-temporal relational databases. This model suits well the requirements of off-line applications dealing with average-size and known in advance geographic data that can be represented in tabular form. This chapter explores off-line maritime data processing in such relational databases and provides a step-by-step guide to build a maritime database for investigating maritime traffic and vessel behaviour. Along the chapter, examples and exercises are proposed to build a maritime database using the data available in the open, heterogeneous, integrated dataset for maritime intelligence, surveillance, and reconnaissance that is described in [41]. The dataset exemplifies the variety of data that are nowadays available for monitoring the activities at sea, mainly the Automatic Identification System (AIS), which is openly broadcast and provides worldwide information on the maritime traffic. All the examples and the exercises refer to the syntax of the widespread relational database management system PostgreSQL and its spatial extension PostGIS, which are an established and standard-based combination for spatial data representation and querying. Along the chapter, the reader is guided to experience the spatio-temporal features offered by the database management system, including spatial and temporal data types, indexes, queries and functions, to incrementally investigate vessel behaviours and the resulting maritime traffic.
dc.language.isoen
dc.publisherSpringer International Publishing
dc.publisher.locationCham
dc.source.titleGuide to Maritime Informatics
dc.title.enMaritime Data Processing in Relational Databases
dc.typeChapitre d'ouvrage
dc.identifier.doi10.1007/978-3-030-61852-0_3
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halPhysique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an]
dc.subject.halInformatique [cs]/Base de données [cs.DB]
bordeaux.page73-118
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
hal.identifierhal-03137050
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03137050v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Guide%20to%20Maritime%20Informatics&rft.date=2021-02-09&rft.spage=73-118&rft.epage=73-118&rft.au=ETIENNE,%20Laurent&RAY,%20Cyril&CAMOSSI,%20Elena&IPHAR,%20Cl%C3%A9ment&rft.genre=unknown


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