The 2018 Grand Challenge targets the problem of accurate predictions on data streams produced by automatic identification system (AIS) equipment, describing naval traffic. This paper reports the technical details of a custom solution, which exposes multiple tuning parameters, making its configurability one of the main strengths. Our solution employs a cell grid architecture essentially based on a sequence of hash tables, specifically built for the targeted use case. This makes it particularly effective in prediction on AIS data, obtaining a high accuracy and scalable performance results. Moreover, the architecture proposed accommodates also an optionally semi-supervised learning process besides the basic supervised mode.
@article{arxiv.1810.00090,
title = {Cell Grid Architecture for Maritime Route Prediction on AIS Data Streams},
author = {Ciprian Amariei and Paul Diac and Emanuel Onica and Valentin Roşca},
journal= {arXiv preprint arXiv:1810.00090},
year = {2018}
}