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Using Enterprise Architecture Models and Bayesian Belief Networks for Failure Impact Analysis
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
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2009 (English)In: Lecture Notes in Computer Science: Service-Oriented Computing, 2009, 339-350 p.Conference paper, Published paper (Refereed)
Abstract [en]

The increasing complexity of enterprise information systems makes it very difficult to prevent local failures from causing ripple effects with serious repercussions to other systems. This paper proposes the use of Enterprise Architecture models coupled with Bayesian Belief Networks to facilitate Failure Impact Analysis. By extending the Enterprise Architecture models with the Bayesian Belief Networks we are able to show not only the architectural components and their interconnections but also the causal influence the availabilities of the architectural elements have on each other. Furthermore, by using the Diagnosis algorithm implemented in the Bayesian Belief Network tool GeNIe, we are able to use the network as a Decision Support System and rank architectural components with their respect to criticality for the functioning of a business process. An example featuring a car rental agency demonstrates the approach.

Place, publisher, year, edition, pages
2009. 339-350 p.
Series
Lecture notes in computer science, ISSN 0302-9743 ; 5472
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-102859DOI: 10.1007/978-3-642-01247-1_35ISI: 000266107600030Scopus ID: 2-s2.0-70350630596OAI: oai:DiVA.org:kth-102859DiVA: diva2:557120
Conference
6th International Conference on Service-Oriented Computing, Sydney, AUSTRALIA, 2008
Note

QC 20121003

Available from: 2012-09-27 Created: 2012-09-27 Last updated: 2012-10-03Bibliographically approved

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  • apa
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Output format
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