Scalable and Distributed Sea Port Operational Areas Estimation from AIS Data

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 i...

Full description

Saved in:
Bibliographic Details
Published in2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) pp. 374 - 381
Main Authors Millefiori, Leonardo M., Zissis, Dimitrios, Cazzanti, Luca, Arcieri, Gianfranco
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
AIS
Apache Spark
Artificial intelligence
big data
Data mining
Decision making
Europe
KDE
MapReduce
Marine vehicles
port location estimation
Ports (Computers)
Rotterdam port
Sea measurements
Online AccessGet full text

Cover

More Information
Summary: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 well-known KDE algorithm to the MapReduce paradigm, and report results on the port of Rotterdam.
ISSN:2375-9259
DOI:10.1109/ICDMW.2016.0060