On Optimization of Predictions in Ontology-Driven Situation Awareness
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management (KSEM 2009), Vienna, Austria
Original Kurzfassung:
Systems supporting situation awareness in large-scale control
systems, such as, e. g., encountered in the domain of road traffic management,
pursue the vision of allowing human operators prevent critical
situations. Recently, approaches have been proposed, which express situations,
their constituting objects, and the relations in-between (e. g.,
road works causing a traffic jam), by means of domain-independent ontologies,
allowing automatic prediction of future situations on basis of
relation derivation. The resulting vast search space, however, could lead
to unacceptable runtime performance and limited expressiveness of predictions.
In this paper, we argue that both issues can be remedied by
taking inherent characteristics of objects into account. For this, an ontology
is proposed together with optimization rules, allowing to exploit
such characteristics for optimizing predictions. A case study in the domain
of road traffic management reveals that search space can be substantially
reduced for many real-world situation evolutions, and thereby
demonstrates the applicability of our approach.
Forschungs-