SUPPORTING ENTERPRISE TRANSFORMATION USING A UNIVERSAL MODEL ANALYSIS APPROACH

Enterprise Architecture Management has been proposed to help organizations in their efforts to flexibly adapt to rapidly changing market environments. Enterprise architectures are described by means of conceptual models depicting, e.g., an enterprise?s business processes, its organisational structure, or the data the enterprise needs to manage. Such models are stored in large repositories. Using these repositories to support enterprise transformation processes often requires detecting structural patterns containing particular labels within the model graphs. As an example, consider the case of mergers and acquisitions. Respective patterns could represent specific model fragments that occur frequently within the process models of the merging companies. This paper introduces an approach to analyse conceptual models at a structural and semantic level. In terms of structure, the approach is able to detect patterns within the model graphs. In terms of semantics, the approach is able to detect previously standardized model labels. Its core contribution to enterprise architecture management and transformation is two-fold. First, it is able to analyse conceptual models created in arbitrary modelling languages. Second, it supports a wide variety of pattern-based analysis tasks related to managing change in organisations. The approach is applied in a merger and acquisition scenario to demonstrate its applicability

SUPPORTING TERMINOLOGICAL STANDARDIZATION IN CONCEPTUAL MODELS - A PLUGIN FOR A META-MODELLING TOOL

Today´s enterprises are accumulating huge repositories of conceptual models, such as data models, organisational charts and most notably business process models. Those models often grow heterogeneously with the company and are thus often terminologically divers and complex. This terminological diversity originates from the fact that natural language allows an issu to be described in a large variety of ways especially when many modellers are involved. This diversity can become a pitfall when conceptual models are subject to model analysis techniqus, which require terminologically comparable model elements. Therefore, it is essential to ensure model quality by enforcing naming conventions. This paper introduces a prototype, which intends to resolve all associated issus of terminological standardisation already during the modelling phase or ex-post based on existing models. The modeller is guided through the standardization process by providing an automated list of all correct phrase propositions according to his entered phrase. In this approach, naming conventions can easily be defined and enforced. This leads to terminologically unambiguous conceptual models, which are easier to understand and ready for further analysis purposes