Temporal reasoning based on semi-intervals
Artificial Intelligence
Qualitative Spatial Reasoning with Conceptual Neighborhoods for Agent Control
Journal of Intelligent and Robotic Systems
Forecasting Stock Price Index Using Fuzzy Time-Series Based on Rough Set
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
A software architecture for ontology-driven situation awareness
Proceedings of the 2008 ACM symposium on Applied computing
Ontology-based situation awareness
Information Fusion
On combinations of binary qualitative constraint calculi
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
A bayesian network approach to traffic flow forecasting
IEEE Transactions on Intelligent Transportation Systems
Towards duplicate detection for situation awareness based on spatio-temporal relations
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems: Part II
Situation prediction nets: playing the token game for ontology-driven situation awareness
ER'10 Proceedings of the 29th international conference on Conceptual modeling
Making workflows situation aware: an ontology-driven framework for dynamic spatial systems
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
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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.