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Big Data and Data Analytics for Ports of the Future, COREALIS

Authors: Tsimiklis, Georgios; Nikolopoulou, Amalia; Amditis, Angelos; Morillo, Carlos; Jihed Khiari;

Big Data and Data Analytics for Ports of the Future, COREALIS

Abstract

COREALIS proposes a strategic, innovative framework, supported by disruptive technologies, including IoT, data analytics, next generation traffic management, for modern ports to handle future capacity, traffic, efficiency and environmental challenges. Through COREALIS, the port will minimize its environmental footprint to the city, it will decrease disturbance to local population through a reduction in the congestion around the port. COREALIS proposes a bunch of solutions for the ports based on big data analytics: i) a predictive model for maintenance for the port of Piraeus ii) a cargo flow optimizer based on historical and current data for hinterland connection applying at the Port of Antwerp iii) an optimized Truck Appointment System based on an Estimated Time of Arrival through real time data.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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