  Ship trajectories from Automatic Identification System (AIS) messages are
important in maritime safety, domain awareness, and algorithmic testing.
Although the specifications for transmitting and receiving AIS messages are
fixed, it is well known that technical inaccuracies and lacking seafarer
compliance lead to severe data quality impairment. This paper proposes an
adaptable, data-driven, $\alpha$-quantile-based framework for decoding,
constructing, splitting, and assessing trajectories from raw AIS records to
improve transparency in AIS data mining. Results indicate the proposed
filtering algorithm robustly extracts clean, long, and uninterrupted
trajectories for further processing. An open-source Python implementation of
the framework is provided.
