Reasoning on Data Streams for Situation Awareness

Information overload is a severe problem for human operators of large-scale control systems, for instance, in road traffic management. In order to determine a complete and coherent view of the overall situation (i. e., gain situation awareness), an operator of such a system must consider various heterogeneous sources providing streams of information about a large number of real-world objects. Since the usage of ontologies has been regarded to be beneficial for achieving situation awareness, various ontology-driven situation awareness systems have been proposed. Coping with evolving and volatile individuals in ontologies, however, has not been their focus up to now. In this paper, we describe how concepts from data stream management systems and stream reasoning, such as sliding windows, continuous queries, and incremental reasoning, can be adjusted to support reasoning over highly dynamic ontologies for situation awareness. We conclude our paper with a prototypical implementation and a discussion of lessons learned, pointing to directions of future work.

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