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Maritime Data Processing in Relational Databases


Maritime 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.
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hal-03137050 , version 1 (10-02-2021)



Laurent Etienne, Cyril Ray, Elena Camossi, Clément Iphar. Maritime Data Processing in Relational Databases. Guide to Maritime Informatics, Springer International Publishing, pp.73-118, 2021, ⟨10.1007/978-3-030-61852-0_3⟩. ⟨hal-03137050⟩
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