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The 2018 Grand Challenge targets the problem of accurate predictions on datastreams produced by automatic identification system (AIS) equipment, describingnaval traffic. This paper reports the technical details of a custom solution,which exposes multiple tuning parameters, making its configurability one of themain strengths. Our solution employs a cell grid architecture essentially basedon a sequence of hash tables, specifically built for the targeted use case.This makes it particularly effective in prediction on AIS data, obtaining ahigh accuracy and scalable performance results. Moreover, the architectureproposed accommodates also an optionally semi-supervised learning processbesides the basic supervised mode.

Cell Grid Architecture for Maritime Route Prediction on AIS Data Streams