Situation Prediction Nets - Playing the Token Game for Ontology-Driven Situation Awareness
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 29th International Conference on Conceptual Modeling (ER)
Original Kurzfassung:
Situation awareness in large-scale control systems such as
road traffic management aims to predict critical situations on the basis
of spatio-temporal relations between real-world objects. Such relations
are described by domain-independent calculi, each of them focusing on a
certain aspect, for example topology. The fact that these calculi are described
independently of the involved objects, isolated from each other,
and irrespective of the distances between relations leads to inaccurate
and crude predictions. To improve the overall quality of prediction while
keeping the modeling effort feasible, we propose a domain-independent
approach based on Colored Petri Nets that complements our ontology-driven
situation awareness framework BeAware!. These Situation Prediction
Nets can be generated automatically and allow increasing (i) prediction
precision by exploiting ontological knowledge in terms of object characteristics
and interdependencies between relations and (ii) increasing
expressiveness by associating multiple distance descriptions with transitions.
The applicability of Situation Prediction Nets is demonstrated
using real-world traffic data.
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