A Probabilistic Enterprise Architecture Model Evolution

Abstract

Enterprise Architecture (EA) is a widely accepted means to ease the alignment of IS projects with enterprisewide objectives. One central artifact of EA are EA models, which provide a holistic view on the organization and support EA's stakeholder to create added value. As EA collects its data from different sources, the data can be contradictory. This work contributes to existing research by proposing a novel approach to deal with contradictory data without solving the thereby caused conflicts. In order to achieve this objective, we refine the Predictive, Probabilistic Architecture Modeling Framework (P 2 AMF) introduced by Johnson et al., which already incorporates a way to represent uncertainty regarding the existence of modelled entities. To make our technique usable, we generalize P 2 AMF from its UML/OCL notation to a graph presentation in order to apply it to EA models notated in arbitrary notations like ArchiMate. Furthermore, we add alternative scenarios in different versions along a time series to meet the requirements of a distributed EA evolution. To show the applicability of our approach, we developed a proof of concept prototype by implementing the proposed calculations and guidelines on a Neo4j graph database. Last, we argue that our approach meets the stated requirements of a distributed EA evolution.

A Continuous Delivery Pipeline for EA Model Evolution

“…Additionally, Fischer et al define four roles, which are either related to process coordination (EA Coordinator, EA Repository Manager), data delivery (Data Owner), or quality checks (EA Coordinator, EA Stakeholder). Last, we presented a process for a distributed EA model evolution [13] describing different tasks and their sequence focusing on a continuous evolution of the EA model. -Design & Development: To realize an artifact in accordance to the beforehand identified objectives, first, we align the input of the three objectives' sources.…”

“…Fischer et al [10] contribute two main findings to our pipeline. First, they propose an EA model maintenance process, which we unite with our work from [13]. Second, they offer a fine-grained role concept, which we incorporate in the pipeline as well.…”

“…All existing artifact are now processed in the next transformation activity "Align Model Data". Hacks and Lichter [13] argue that a specific project may contain more detailed information than the more general global EA model. Therefore, the model provided by the project has to be aligned in order to be effectively compare-and merge-able to the central model.…”

“…To overcome the aforementioned problems of EA modeling, we proposed already an architecture roundtrip process [13]. However, this process is still abstract and needs to be instantiated.…”

“…[22,38]), or proposing an overall process for maintenance (e.g. [9,13]). To the best of our knowledge, there is no research around trying to adapt the technique of CD to the domain of EA model maintenance.…”