Abstract
The maritime security domain is challenged by a number of
data analysis needs focusing on increasing the maritime
situation awareness, i.e., detection and analysis of abnormal
vessel behaviors and suspicious vessel movements. The need
for efficient processing of dynamic and/or static vessel data
that come from different heterogeneous sources is emerged. In
this paper we describe how we address the challenge of
combining and processing real-time and static data from
different sources using ontology-based data access
techniques, and we explain how the application of semantic
web technologies increases the value of data and improves the
processing workflow in the maritime domain.
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