Reference : Reasoning at Runtime using time-distorted Contexts: A Models@run.time based Approach

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/17637

Title :	Reasoning at Runtime using time-distorted Contexts: A Models@run.time based Approach
Language :	English
Author, co-author :	Hartmann, Thomas [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Fouquet, François [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Nain, Grégory [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Morin, Brice [SINTEF ICT Norway]
	Klein, Jacques [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Jul-2014
Edition :	1st
Main document title :	Proceedings of the 26th International Conference on Software Engineering and Knowledge Engineering
Publisher :	Knowledge Systems Institute Graduate School, USA
Pages :	586-591
Peer reviewed :	Yes
Audience :	International
ISBN :	1-891706-35-7
City :	Vancouver
Country :	Canada
Event name :	26th International Conference on Software Engineering and Knowledge Engineering
Event date :	1-7-2014 to 3-7-2014
Event organizer :	Knowledge Systems Institute Graduate School, USA
Event place (city) :	Vancouver
Event country :	Canada
Keywords :	[en] Temporal data ; Time-aware context modeling ; Knowledge representation ; Reactive systems ; Intelligent systems
Abstract :	[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.
Permalink :	http://hdl.handle.net/10993/17637
Commentary :	The research leading to this publication is supported by the National Research Fund Luxembourg (grant 6816126) and Creos Luxembourg S.A. under the SnT-Creos partnership program.

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