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Astract: This paper reports on the plenary panel discussion on "How to Build a Perfect Enterprise Modeling Method" held at the PoEM 2021 conference. The panel was charged with finding a pathway to the perfect enterprise modeling method. The panelists have a background in enterprise modeling and method engineering. So, the question is: Can method engineering help with designing a better enterprise modeling environment? The contributions in this paper should be regarded as a snapshot of our viewpoints at the time of the panel.

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Astract: The heterogeneity in enterprise design stakeholders and models gen- erally demands for consistent and efficient transformations of enterprise design knowledge between different conceptual modelling languages. A systematic pro- cess and precise model transformation specifications are a prerequisite for realizing such transformations. The Design and Engineering Methodology for Organiza- tions (DEMO) approach represents the organization design of an enterprise in four linguistically based, semantically sound aspect models. The Business Pro- cess Model and Notation (BPMN) on the other hand enables more flexibility in creating models and benefits from wide adoption in industry, the execution of pro- cesses e.g., by simulations, and the availability of proper tooling. A transformation of DEMO models into BPMN models is thus desirable to avail of both, the seman- tic sound foundation of DEMO and the wide adoption and execution possibilities of BPMN. Previous research already developed some principles and practices for transforming DEMO models into BPMN models, based on DEMOSL 3.7. This study focuses on the latest DEMO language specification, DEMOSL 4.5, since we believe that more clarity is required to specify consistent, well-motivated trans- formation specifications. We present a list of main requirements for developing transformation specifications to transform concepts represented in a Coordination Structure Diagram and Process Structure Diagram of DEMO into corresponding concepts in a BPMN collaboration diagram. The article makes three contribu- tions: (1) Generic requirements for developing DEMO-to-BPMN transformation specifications; (2) Nine transformation scenarios that are validated by multiple demonstration cases; and (3) A comprehensive college case that demonstrates all transformation scenarios.

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Astract: Semantic processing of conceptual models is a focus of research since several years, bridging the disciplines of knowledge-based systems, conceptual modeling, and model-driven software engineering. With the uptake of Knowledge Graphs, the research in this area gained further momentum. In this paper, we introduce a generic and extensible platform that enables the automated transformation of conceptual models into Knowledge Graphs. The platform can transform any model created by a state-of-the-art metamodeling platform (EMF and ADOxx) into standardized Knowledge Graph representations like GraphML, RDF, and OWL. In the paper at hand, we introduce our platform and evaluate it with a corpus of 5.000 UML models that we transform into Knowledge Graphs and subsequently exemplify the rich functionalities enabled by the graph structure by an automated detection of UML model smells.

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Astract: The Digital Transformation of our society requires IT infrastructures to be more agile, more adaptive, and more connected than ever. At the same time, the owners of such infrastructures are confronted with an increase in regulatory pressure (e.g., the GDPR). These developments put a lot of stress on IT management and governance. To enable IT management and governance to better deal with these challenges, we propose to digitally transform IT governance and management itself by using a Digital Twin based approach. In line with this, we aim to create on ontology-driven Digital Twin for Governed IT Management (DT4GITM) framework. The goal of this framework is to act as a reference architecture for a Digital Twin based infrastructure that connects three interrelated systems: the IT governance processes, the governed IT management processes, and the managed organizational IT assets. The core of the framework involves a generic Governed IT Management (GITM) Domain Ontology, which is planned to be operationalized by a Knowledge Graph based approach that realizes an integrated view on the heterogenous data streams originating from the IT governance and management processes, and the managed IT assets. In this paper, we start by outlining the planned DT4GITM framework and the pivotal role of the GITM Domain Ontology within this. We then elaborate our incremental, and scenario- and case-driven strategy towards the development of the framework as a whole, and the GITM Domain Ontology in particular. This is followed by the elaboration of a specific DT4GITM scenario which serves as a first proof of principle.

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Astract: A core strength of enterprise architecture (EA) models is their holistic and integrative nature. With ArchiMate, a de-facto industry standard for modeling EAs is available and widely adopted. However, with the growing complexity of enterprise operations and IT infrastructures, EA models grow in complexity. Research showed that ArchiMate as a language and the supporting EA tools lack advanced visualization and analysis functionality. This paper proposes a generic and extensible framework for transforming EA models into graph structures to enable the automated analysis of even huge EA models. We show how enterprise architects can bene t from the vast number of graph metrics during decision-making. We also describes the implementation of the extensible Graph-based Enterprise Architecture Analysis (eGEAA) Cloud platform that supports the framework. The evaluation of our approach and platform con rms feasibility and interoperability with third-party tools.

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Astract: Microservices Architectures (MSAs) are continuously replacing monolithic systems toward achieving more flexible and maintainable service-oriented software systems. However, the shift toward an MSA also requires a technological and managerial shift for its adopters. Architecting and managing MSAs represent unique challenges, including microservices´ identification, interoperability, and reuse. To handle these challenges, we propose an Ontology-driven Conceptual Modelling approach, based on the Ontology of Microservices Architecture Concepts (OMSAC), for modelling and analyzing microservices-based systems. We show, how OMSAC-based conceptual models, stocked in a Stardog triple store, support Stakeholder-specific communication, documentation, and reuse. This paper reports on the application of our approach in three open-source MSA systems with a focus on microservices´ discovery based on similarity metrics. Eventually, we compare the extracted similarity metrics derived from the application of machine learning techniques to the OMSAC models with a manual analysis performed by experts.

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Astract: The Entity Relationship model is the de-facto standard for data modeling and has been in use for a long time already. This popularity also led to the development of various tools that support ER modeling. However, these tools are often inflexible, proprietary, constrained to specific platforms, and lack advanced features like (SQL) code generation. This paper introduces the bigER modeling tool. bigER offers various features for flexibly specifying and visualizing conceptual ER data models. Within the VS Code IDE, the tool enables hybrid and interactive modeling through a textual editor with a custom language to specify ER elements and an accompanying view to display and modify the graphical ER model. The bigER modeling tool is one of the first tools to incorporate the Language Server Protocol and to be distributed through the VS Code ecosystem. Due to its web technology-based architecture, it is platform-independent and easily extensible.

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Astract: If an Enterprise Model is to be used in the strategic decision-making, it should represent the connections between the elements of internal structure, like processes, machines, people, and the elements of the business environment, like market segments, competitors, regulators. The paper presents and discusses a modeling technique - Fractal Enterprise Model (FEM) - that allows to represent such connections, and a computerized toolkit - FEM toolkit - that supports the modeling process. The presentation is done based on a running example. The attendees will learn about an innovative presentation of a business environment in an enterprise model.

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Astract: Hitherto, the concept of Enterprise Architecture (EA) Smells has been proposed to assess quality flaws in EAs and their models. Together with this new concept, a catalog of different EA Smells has been published and a first prototype was developed. However, this prototype is limited to ArchiMate and is not able to assess models adhering to other EA modeling languages. Moreover, the prototype is not integrate-able with other EA tools. Therefore, we propose to enhance the extensible Graph-based Enterprise Architecture Analysis (eGEAA) platform that relies on Knowledge Graphs with EA Smell detection capabilities. To align these two approaches, we show in this paper, how ArchiMate models can be transformed into Knowledge Graphs and provide a set of queries on the Knowledge Graph representation that are able to detect EA Smells. This enables enterprise architects to assess EA Smells on all types of EA models as long as there is a Knowledge Graph representation of the model. Finally, we evaluate the Knowledge Graph based EA Smell detection by analyzing a set of 347 EA models.

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Astract: Die Baubranche wird durch die Entwicklung digitaler Systeme in der Planung und Abwicklung von Bauprojekten sukzessiv beeinflusst. Dabei setzen Unternehmen und Auftraggeber je nach Größe auf Eigenentwicklungen oder auf dem Markt erhältliche Produkte. Dieser Artikel zeigt, welche Möglichkeiten bestehen, um die Erstellung und Einführung digitaler Systeme in der Baubranche qualitativ und quantitativ zu bewerten. Dafür werden Methoden für die Entwicklung und Etablierung softwaregestützter Prozesse auf Baustellen vorgestellt.