  In this work, we investigate a novel semantic approach for pattern discovery
in trajectories that, relying on ontologies, enhances object movement
information with event semantics. The approach can be applied to the detection
of movement patterns and behaviors whenever the semantics of events occurring
along the trajectory is, explicitly or implicitly, available. In particular, we
tested it against an exacting case scenario in maritime surveillance, i.e., the
discovery of suspicious container transportations.
  The methodology we have developed entails the formalization of the
application domain through a domain ontology, extending the Moving Object
Ontology (MOO) described in this paper. Afterwards, movement patterns have to
be formalized, either as Description Logic (DL) axioms or queries, enabling the
retrieval of the trajectories that follow the patterns.
  In our experimental evaluation, we have considered a real world dataset of 18
Million of container events describing the deed undertaken in a port to
accomplish the shipping (e.g., loading on a vessel, export operation).
Leveraging events, we have reconstructed almost 300 thousand container
trajectories referring to 50 thousand containers travelling along three years.
We have formalized the anomalous itinerary patterns as DL axioms, testing
different ontology APIs and DL reasoners to retrieve the suspicious
transportations.
  Our experiments demonstrate that the approach is feasible and efficient. In
particular, the joint use of Pellet and SPARQL-DL enables to detect the
trajectories following a given pattern in a reasonable time with big size
datasets.
