At this year’s PoEM, Simon and Dominik will present the work of their student Robin, who developed an extension to the CM2KG to be able to store historical data and the differences between the single states of the model. This will allow a first step into the direction to analyze EA Smells that consider changes over time. So far, it is just possible to identify EA Smells that appear in each and every state of an EA model.
Enterprise Architecture (EA) is the discipline that aims to provide a holistic view of the enterprise by explicating business and IT alignment from the perspectives of high-level corporate strategy down to daily operations and network infrastructures. EAs are consequently complex as they compose and integrate many aspects on different architecture layers. A recent proposal to cope with this complexity and to make EAs amenable to automated and intuitive visual analysis is the transformation of EA models into EA Knowledge Graphs. A remaining limitation of these approaches is that they perceive the EA to be static, i.e., they represent and analyze EAs at a single point in time. In the paper at hand, we introduce a historization concept, a prototypical implementation, and a performance analysis for how EAs can be represented and processed to enable the analysis of their evolution.
In: Barn, Balbir S.; Sandkuhl, Kurt (Ed.): The Practice of Enterprise Modeling. PoEM 2022, Springer International Publishing, Cham, 2022.
Using Knowledge Graphs to Detect Enterprise Architecture Smells Proceedings Article
In: Serral, Estefanía; Stirna, Janis; Ralyté, Jolita; Grabis, Jānis (Ed.): The Practice of Enterprise Modeling, pp. 48–63, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-91279-6.
In: Ghose, Aditya; Horkoff, Jennifer; Souza, Vitor E. Silva; Parsons, Jeffrey; Evermann, Joerg (Ed.): 40th International Conference on Conceptual Modeling, Springer Springer, LNCS, 2021.