Handling and Analyzing Marine Traffic Data

Abstract

With the emergence of the Automatic Identification System (AIS), the ability to track and analyze vessel behaviour within the marine domain was introduced. Nowadays, the ubiquitous availability of huge amounts of data presents challenges for systems aimed at using AIS data for analysis purposes regarding computability and how to extract valuable information from the data. This thesis covers the process of developing a system capable of performing AIS data analytics using state of the art Big data technologies, supporting key features from a system called Marine Traffic Analyzer 3. The results show that the developed system has improved performance, supports larger files and is accessible by more users at the same time. Another problem with AIS is that since the technology was initially constructed for collision avoidance-purposes, there is no solid mechanism for data validation. This introduces several issues, among them is what is called identity fraud, that is when a vessel impersonates another vessel for various malicious purposes. This thesis explores the possibility of detecting identity fraud by using clustering techniques for extracting voyages of vessels using movement patterns and presents a prototype algorithm for doing so. The results concerning the validation show some merits, but also exposes weaknesses such as time consuming tuning of parameters.