Big Data and Big Water: Mining Ocean Vessel Trajectory Data

In this presentation we will focus on the ongoing work in exploration and analysis of data from ocean vessel movements, using the Automatic Identification System (AIS) data. We will discuss some of the challenges and benefits related to the large-scale exploration and analysis of AIS data. We will look at detection of anomalous trajectories of ships in mid-ocean and in port vicinity, and at the ecologically-oriented detection and analysis of data related to fishing activities. We will discuss our early results in these select applications, including data representation and data modeling techniques, particularly the clustering techniques, classification, and attribute engineering used in our work. We will round up with discussion of potential future work with AIS data.

Short bio: Stan Matwin is a Canada Research Chair, a Professor of Computer Science, and the Director of the Institute for Big Data Analytics at Dalhousie University. He is also Distinguished University Professor of Electrical Engineering and Computer Science at the University of Ottawa, and affiliated with the Institute for Computer Science, Polish Academy of Sciences. Stan's broad research interests include: Data and Text Mining, Text Analytics, Artificial Intelligence, Machine Learning, Data Privacy and Bioinformatics.

Stan is currently visiting the Knowledge Discovery and Data Mining Laboratory, KDD Lab, at University of Pisa and ISTI-CNR.