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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.