Predicting Ship Behavior Navigating through Heavily Trafficked Fairways by Analyzing AIS Data on Apache HBase

The newly developed Big Data oriented distributed systems such as Apache HBase have been proven effective in storing and analyzing the exponentially growing volume of wide variety of data such as sensors data, customer generated media, web logs, and so forth. In this paper, Apache HBase, a distributed scalable big data store, is used to store, process, and analyze a large amount of spatiotemporal data generated by shipboard AIS transponders. The objective is to predict the behavior of ships navigating through heavily trafficked fairways around the gates of busy harbors. For that purpose, experiments were conducted using tens of gigabytes of real world AIS data. The data were processed to form historical ships´ tracks and were classified based on ships attributes such as type, draught, voyage destination and country of origin. Finally, a simple algorithm was implemented to predict the target ships behavior based on its attributes and movement characteristic. As a result, an acceptable prediction of ships movement is achieved. Furthermore, the experimental result also indicated that in the case of data processing speed, this technique remarkably outperformed the traditional GIS application software.