Latest revision as of 22:19, 1 February 2021

Abstract

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems.

Document type: Article

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The different versions of the original document can be found in:

• http://publica.fraunhofer.de/documents/N-502913.html

• https://figshare.com/articles/MARITIME_DATA_INTEGRATION_AND_ANALYSIS_RECENT_PROGRESS_AND_RESEARCH_CHALLENGES/7679360

• https://openaccess.city.ac.uk/id/eprint/19854/1/Maritime%20data.pdf

• https://zenodo.org/record/2557432 under the license http://creativecommons.org/licenses/by/4.0/legalcode

• https://zenodo.org/record/322384 under the license http://creativecommons.org/licenses/by-sa/4.0/legalcode

• https://zenodo.org/record/2557432,

http://dx.doi.org/10.5281/zenodo.2557431 under the license http://creativecommons.org/licenses/by/4.0/legalcode

• https://zenodo.org/record/2557432,

http://dx.doi.org/10.5281/zenodo.2557432 under the license http://creativecommons.org/licenses/by/4.0/legalcode

DOIS: 10.5281/zenodo.2557432 10.5441/002/edbt.2017.18 10.5281/zenodo.2557431