Scalable and Distributed Sea Port Operational Areas Estimation from AIS Data

• Published in: 2016 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
• Language: English
• 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
• DOI: 10.1109/ICDMW.2016.0060

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.