Knowledge extraction from maritime spatiotemporal data: An evaluation of clustering algorithms on Big Data

Abstract

In this paper we attempt to define the major trade routes which vessels of trade follow when travelling across the globe in a scalable, data-driven unsupervised way. For this, we exploit a large volume of historical AIS data, so as to estimate the location and connections of the major trade routes, with minimal reliance on other sources of information. We address the challenges posed due to the volume of data by leveraging distributed computing techniques and present a novel MapReduce based algorithmic approach, capable of handling skewed and nonuniform geospatial data. In the direction, we calculate and compare the performance (execution time and compression ratio) and accuracy of several mature clustering algorithms and present preliminary results.