Big Water Meets Big Data: Analytics of the AIS Ship Tracking Data
Stan Matwin
DOI: http://dx.doi.org/10.15439/2016F598
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 1–1 (2016)
Abstract. Oceans constitute 70\% of the globe and are an important component of the human environment. Their impact on the climate, ecology and economics cannot be overestimated. And yet analysis of large, ocean-generated datasets has lagged behind other fields, such as medicine, business and bioinformatics. This situation is currently changing, and one of the reasons is the advent of the global, IMO-mandated, satellite-borne ship tracking system known as Automatic Identification System (AIS). AIS data is an excellent example of Big Data and requires innovative data management, analysis and visualization techniques. In this presentation I will outline and discuss current Machine Learning and Data Mining projects using AIS data, under way at the Institute for Big Data Analytics at Dalhousie University, Halifax, Canada.