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

@article{Spiliopoulos2017KnowledgeEF,
  title={Knowledge extraction from maritime spatiotemporal data: An evaluation of clustering algorithms on Big Data},
  author={Giannis Spiliopoulos and Konstantinos Chatzikokolakis and Dimitrios Zissis and Evmorfia Biliri and Dimitrios Papaspyros and Giannis Tsapelas and Spyros Mouzakitis},
  journal={2017 IEEE International Conference on Big Data (Big Data)},
  year={2017},
  pages={1682-1687}
}
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… 

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