Reference : A Temporal Model for Interactive Diagnosis of Adaptive Systems

	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/10993/36721

Title :	A Temporal Model for Interactive Diagnosis of Adaptive Systems
Language :	English
Author, co-author :	Mouline, Ludovic [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Benelallam, Amine [Univ Rennes > Inria, CNRS, IRISA > DiverSE]
	Fouquet, François [DataThings S.A.R.L.]
	Bourcier, Johann [Univ Rennes > Inria, CNRS, IRISA > DiverSE]
	Barais, Olivier [Univ Rennes > Inria, CNRS, IRISA > DiverSE]
Publication date :	Sep-2018
Main document title :	2018 IEEE International Conference on Autonomic Computing (ICAC)
Author, co-author :	Mouline, Ludovic
	Benelallam, Amine
	Fouquet, François
	Bourcier, Johann
	Barais, Olivier
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	IEEE International Conference on Autonomic Computing (ICAC)
Event date :	from 03-09-2018 to 07-09-2018
Event place (city) :	Trento
Event country :	Italy
Keywords :	[en] Adaptive systems ; Traceability ; Diagnosis ; model-driven engineering
Abstract :	[en] The evolving complexity of adaptive systems impairs our ability to deliver anomaly-free solutions. Fixing these systems require a deep understanding on the reasons behind decisions which led to faulty or suboptimal system states. Developers thus need diagnosis support that trace system states to the previous circumstances –targeted requirements, input context– that had resulted in these decisions. However, the lack of efficient temporal representation limits the tracing ability of current approaches. To tackle this problem, we first propose a knowledge formalism to define the concept of a decision. Second, we describe a novel temporal data model to represent, store and query decisions as well as their relationship with the knowledge (context, requirements, and actions). We validate our approach through a use case based on the smart grid at Luxembourg. We also demonstrate its scalability both in terms of execution time and consumed memory.
Permalink :	http://hdl.handle.net/10993/36721

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