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A big data architecture for managing oceans of data and maritime applications

 
: Lytra, I.; Vidal, M.-E.; Orlandi, F.; Attard, J.

:

Jardim-Gonçalves, Ricardo (Ed.) ; Institute of Electrical and Electronics Engineers -IEEE-:
23rd International Conference on Engineering, Technology and Innovaiton, ICE/ITMC 2017. Proceedings : 27-29 June 2017, Madeira Island, Portugal
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-0774-9
ISBN: 978-1-5386-0775-6
S.1216-1226
International Conference on Engineering, Technology and Innovation (ICE) <23, 2017, Madeira>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
Data in the maritime domain is growing at an unprecedented rate, e.g., terabytes of oceanographic data are collected every month, and petabytes of data are already publicly available. Big data from heterogeneous sources such as sensors, buoys, vessels, and satellites could potentially fuel a large number of interesting applications for environmental protection, security, fault prediction, shipping routes optimization, and energy production. However, because of several challenges related to big data and the high heterogeneity of the data sources, such applications are still underdeveloped and fragmented. In this paper, we analyze challenges and requirements related to big maritime data applications and propose a scalable data management solution. A big data architecture meeting these requirements is described, and examples of its implementation in concrete scenarios are provided. The related data value chain and use cases in the context of a European project, BigDataOcean, are also described.

: http://publica.fraunhofer.de/dokumente/N-520486.html