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dc.contributor.authorMillefiori, Leonardo
dc.contributor.authorZissis, Dimitrios
dc.contributor.authorCazzanti, Luca
dc.contributor.authorArcieri, Gianfranco
dc.date.accessioned2019-06-19T10:02:49Z
dc.date.available2019-06-19T10:02:49Z
dc.date.issued2019/06
dc.identifier.govdocCMRE-PR-2019-066en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12489/806
dc.description.abstractSeaports play a vital role in the global economy, as they operate as the connection corridors to all other modes of transport and as engines of growth for the wider region. But ports today are faced with numerous unique challenges and for them to remain competitive, significant investments are required. In support of greater transparency in policy making, decisions regarding investment need to be supported by data-driven intelligence. It is often an overlooked fact that seaports do not remain static over time; such spatial units often evolve according to environmental patterns both in size but also connectivity and operational capacity. As such any valid decision making regarding port investment and policy making, essentially needs to take into account port evolution over time and space. In this work, we leverage the huge amounts of vessel data that are progressively becoming available through the Automatic Identification System (AIS) and distributed machine learning to define a seaport's extended area of operation. Specifically, we present our adaptation of the well-known KDE algorithm to the map-reduce paradigm, and report results on the port of Shanghai.en_US
dc.format6 p. : ill. ; digital, PDF fileen_US
dc.language.isoenen_US
dc.publisherCMREen_US
dc.sourceIn: 2016 IEEE International Conference on Big Data, 5-8 December 2016, Washington DC, USA, pp. 1627-1632, doi: 10.1109/BigData.2016.7840774en_US
dc.subjectMaritime situational awarenessen_US
dc.subjectMaritime securityen_US
dc.subjectInformation fusionen_US
dc.subjectArtificial intelligenceen_US
dc.subjectBig dataen_US
dc.subjectMaritime route extractionen_US
dc.subjectShip movementsen_US
dc.subjectShanghaien_US
dc.subjectPortsen_US
dc.titleA distributed approach to estimating sea port operational regions from lots of AIS dataen_US
dc.typeReprint (PR)en_US
dc.typePapers and Articlesen_US


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