Situation Prediction Nets - Playing the Token Game for Ontology-Driven Situation Awareness
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
29th International Conference on Conceptual Modeling (ER)
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Situation awareness in large-scale control systems such as road traffic management aims to predict critical situations on the basis of spatiotemporal
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.
Sprache der Kurzfassung:
Englisch
Vortragstyp:
Vortrag auf einer Tagung (referiert)
Vortragsdatum:
02.11.2010
Vortragsort:
Kanada
Details zum Vortragsort:
Sauder School of Business, University of British Columbia, Canada