3930660
doi
10.5281/zenodo.3930660
oai:zenodo.org:3930660
RAY, Cyril
Naval Academy Research Institute, France
CAMOSSI, Elena
NATO STO Centre for Maritime Research and Experimentation, Italy
IPHAR, Clément
NATO STO Centre for Maritime Research and Experimentation, Italy
Maritime Data Processing in Relational Databases
ETIENNE, Laurent
LabISEN, France
doi:10.5281/zenodo.1167594
doi:10.1016/j.dib.2019.104141
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
SQL
Maritime informatics
Relational database
Automatic Identification System
<p>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 set of SQL queries has been prepared for the book Maritime Informatics. It aims to help students and teachers explore off-line maritime data processing in relational databases and provides a step-by-step guide to build a maritime database for investigating maritime traffic and vessel behaviour. Examples and exercises of the book chapter are proposed to build a maritime database using the data available in the open, heterogeneous and integrated dataset also available on Zenodo (10.5281/zenodo.1167595). The dataset exemplifies the variety of data that are available as of today 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, an established and standard-based combination for spatial data representation and querying. Along the chapter, through exercises, the reader is guided to handle the various spatio-temporal features offered by the database management system, that include spatial and temporal data types, indexes, queries and functions, and eventually to investigate, incrementally, behaviours of vessels at sea and the state of the maritime traffic.</p>
<p> </p>
Springer
2020-07-05
info:eu-repo/semantics/other
3930659
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https://zenodo.org/records/3930660/files/Maritime Informatics Database Model.png
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https://zenodo.org/records/3930660/files/Maritime_Informatics_SQL_Queries.sql
public
10.5281/zenodo.1167594
Is supplement to
doi
10.1016/j.dib.2019.104141
Is documented by
doi
10.5281/zenodo.3930659
isVersionOf
doi
Maritime Informatics
2020-07-05