Scalable distributed change detection and its application to maritime traffic
Building on a novel methodology based on the Ornstein-Uhlenbeck (OU) process to perform accurate long-term predictions of future positions of ships at sea, we present a statistical approach to the detection of abrupt changes in the process parameter that represents the desired velocity of a ship. Proceeding from well-established change detection techniques, the proposed strategy is also computationally efficient and fit well with big data processing models and paradigms. We report results with a large real-world Automatic Identification System (AIS) data set collected by a network of terrestrial receivers in the Mediterranean Sea from June to August 2016.
SourceIn: 2017 IEEE International Conference on Big Data (Big Data), 11-14 December 2017, Boston, MA, USA, pp. 1650-1657, doi: 10.1109/BigData.2017.8258101