Automatic Probabilistic Enterprise IT Architecture Modeling: A Dynamic Bayesian Networks Approach

Abstract

Enterprise architecture modeling and model maintenance are time- consuming and error-prone activities that are typically performed manually. This position paper presents new and innovative ideas on how to automate the modeling of enterprise architectures. We propose to view the problem of modeling as a probabilistic state estimation problem, which is addressed using Dynamic Bayesian Networks (DBN). The proposed approach is described using a motivating example. Sources of machine-readable data about Enterprise Architecture entities are reviewed.