A Meta-Model for Automated Enterprise Architecture Model Maintenance

Abstract

Maintaining a high quality enterprise architecture (EA) model that is up-to-date and consistent is a difficult but crucial task. The reasons for this difficulty are the size and complexity of EA models, frequent changes in the architecture and the challenge of collecting EA data from different stakeholders in large organizations. In our research project Living IT-Landscape Models we are working towards a tighter synchronization between EA models and what they represent in the real world, thus increasing the model actuality and consistency. With our previous work we have established semi-automated processes for EA data collection and quality assurance. To support these processes an implementing EAM tool needs to be able work with contextual information that is not typically stored alongside the EA models. In the paper at hand we describe a meta-model that incorporates the required context information and can form the basis for EAM tools that support i) recurring data collection from data sources, ii) maintaining relations from imported elements to their sources, iii) storing actuality related characteristics for triggering updates, and iv) identifying properties used to avoid duplicate entries. Related work has acknowledged the relevance of the problem, however no comprehensive approaches to for automating EA model maintenance have been presented yet.