Reference : Model-based time-distorted Contexts for efficient temporal Reasoning
Scientific congresses, symposiums and conference proceedings : Poster
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/18084
Model-based time-distorted Contexts for efficient temporal Reasoning
English
Hartmann, Thomas mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Fouquet, François mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Nain, Grégory mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Brice, Morin mailto [SINTEF ICT Norway]
Klein, Jacques mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Le Traon, Yves mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
2-Jul-2014
Yes
No
International
26th International Conference on Software Engineering and Knowledge Engineering
1-7-2014 to 3-7-2014
Knowledge Systems Institute Graduate School, USA
Vancouver
Canada
[en] Temporal data ; Time-aware context modeling ; Knowledge representation ; Reactive systems ; Intelligent systems
[en] Intelligent systems continuously analyze their context to autonomously take corrective actions. Building a proper knowledge representation of the context is the key to take adequate actions. This requires numerous and complex data models, for example formalized as ontologies or meta-models. As these systems evolve in a dynamic context, reasoning processes typically need to analyze and compare the current context with its history. A common approach consists in a temporal discretization, which regularly samples the context (snapshots) at specific timestamps to keep track of the history. Reasoning processes would then need to mine a huge amount of data, extract a relevant view, and finally analyze it. This would require lots of computational power and be time-consuming, conflicting with the near real-time response time requirements of intelligent systems. This paper introduces a novel temporal modeling approach together with a time-relative navigation between context concepts to overcome this limitation. Similarly to time distortion theory, our approach enables building time-distorted views of a context, composed by elements coming from different times, which speeds up the reasoning. We demonstrate the efficiency of our approach with a smart grid load prediction reasoning engine.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/18084

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