  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.
