Using Enterprise Architecture Models and Bayesian Belief Networks for Failure Impact Analysis

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.