Learning Ship Activity Patterns in Maritime Data Streams: Enhancing CEP Rule Learning by Temporal and Spatial Relations and Domain-Specific Functions

Maritime surveillance systems are of particular importance for the smooth and safe operation of maritime traffic. Such systems must efficiently analyze the continuous stream of incoming ship movement data to provide an up-to-date picture of the situation at sea. Complex Event Processing (CEP) is a software technology dedicated to the analysis of data streams in real time and seems to be promising for maritime surveillance as well. CEP is based on rules that usually have to be formulated manually by domain experts. Recently, several approaches of learning CEP rules have been proposed. However, all these approaches are limited to standard CEP rule languages which are not well suited for describing maritime situations. In this paper, we extend Bat4CEP, an innovative bat-inspired approach to automatic CEP rule learning, to meet the specific needs of the maritime domain. In particular, we extend the rule language of Bat4CEP to include temporal-spatial operators and domain-specific functions. This extended rule language makes it possible to express complex maritime facts in a simple and understandable form. Furthermore, it is shown how the proposed rule language extensions can be integrated into the Bat4CEP learning approach. The effectiveness of the approach is demonstrated through extensive experiments with real maritime data streams.