Semantic Annotations of Enterprise Models for Supporting the Evolution of Model-Driven Organizations

Abstract

In model-driven organizations enterprise models are used to represent and analyze the current and future states of aspects such as strategies, business processes or the enterprise architecture. Thereby, the scope of representation and analysis depends on the used modeling method. Although adaptations of modeling methods are frequently conducted to meet requirements emerging from business changes, such modifications may not be favorable due to potential side effects on other enterprise systems, e.g. through inconsistencies with existing standards and resulting conflicts in algorithmic processing. In the paper at hand we therefore propose the use of semantic annotations of enterprise models for dynamically extending the representation and analysis scope of enterprise modeling methods. Through a loose-coupling between enterprise models and formal semantic schemata, additional information can be represented and processed by algorithms without changes in the original modeling language. In this way, the evolution of information requirements of an organization can be satisfied while maintaining the consistency of the used enterprise modeling languages. For illustrating the feasibility of the approach we describe a use case from the area of risk management. The use case is realized using the SeMFIS platform that supports the annotation of enterprise models and the subsequent machine-based analysis of annotations.