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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: Legacy systems and their associated data models often evolve into large, monolithic artifacts. This threatens comprehensibility and maintainability by human beings. Breaking down a monolith into a modular structure is an established technique in software engineering. Several previous works aimed to adapt modularization also for conceptual data models. However, we currently see a research gap manifested in the absence of: (i) a flexible and extensible modularization concept for Entity Relationship (ER) models; (ii) of openly available tool support; and (iii) empirical evaluation. With this paper, we introduce a generic encoding of a modularization concept for ER models which enables the use of meta-heuristic search approaches. For the efficient application we introduce the ModulER tool. Eventually, we report on a twofold evaluation: First, we demonstrate feasibility and performance of the approach by two demonstration cases. Second, we report on an initial empirical experiment and a survey we conducted with modelers to compare automated modularizations with manually created ones and to better understand how humans approach ER modularization.