dc.contributor.author | Millefiori, Leonardo | |
dc.contributor.author | Zissis, Dimitrios | |
dc.contributor.author | Cazzanti, Luca | |
dc.contributor.author | Arcieri, Gianfranco | |
dc.date.accessioned | 2019-06-19T10:24:45Z | |
dc.date.available | 2019-06-19T10:24:45Z | |
dc.date.issued | 2019/06 | |
dc.identifier.govdoc | CMRE-PR-2019-068 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12489/808 | |
dc.description.abstract | Seaports are spatial units that do not remain static over time. They are constantly in flux, evolving according to environmental and connectivity patterns both in size 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, thus, accurately defining a seaport's exact location, operational boundaries, capacity, connectivity indicators, environmental impact and overall throughput. In this work, we apply a data driven approach to defining a seaport's extended area of operation based on data collected though the Automatic Identification System (AIS). Specifically, we present our adaptation of the wellknown KDE algorithm to the MapReduce paradigm, and report results on the port of Rotterdam. | en_US |
dc.format | 8 p. : ill. ; digital, PDF file | en_US |
dc.language.iso | en | en_US |
dc.publisher | CMRE | en_US |
dc.source | In: 2016 IEEE 16th International Conference on Data Mining Workshops, 12-15 December 2016, Barcelona, Spain, pp. 374-381, doi: 10.1109/ICDMW.2016.0060 | en_US |
dc.subject | Maritime situational awareness | en_US |
dc.subject | Maritime surveillance | en_US |
dc.subject | Maritime security | en_US |
dc.subject | Maritime route extraction | en_US |
dc.subject | Ship movements | en_US |
dc.subject | Ship tracking | en_US |
dc.subject | Automatic Identification Systems (AIS) | en_US |
dc.subject | Rotterdam | en_US |
dc.subject | Ports | en_US |
dc.subject | Big data | en_US |
dc.title | Scalable and distributed sea port operational areas estimation from AIS data | en_US |
dc.type | Reprint (PR) | en_US |
dc.type | Papers and Articles | en_US |