Keywords: modeling tool, Visualization, modeling language, Unified modeling language

Keywords: Modeling tool, Knowledge engineering, Unified Modeling Language, Computer architecture, Model driven engineering, web modeling
Astract: The development of web-based modeling tools still poses significant challenges for developers. The Graphical Language Server Platform (GLSP) reduced some of these challenges by providing the necessary frameworks to efficiently create web modeling tools. However, more knowledge and experience are required regarding developing GLSP-based web modeling tools. This paper discusses the challenges and lessons learned after working with GLSP and realizing several GLSP-based modeling tools. More concretely, experiences, concepts, steps to be followed to develop and operate a GLSP-based web modeling tool, and the advantages and disadvantages of working with GLSP are discussed. As a proof of concept, we will report on the realization of a GLSP-based UML editor called BIGUML. Through BIGUML, we show that our procedure and the reference architecture we developed resulted in a scalable and flexible GLSP-based web modeling tool. The lessons learned, the procedural approach, the reference architecture, and the critical reflection on the challenges and opportunities of using GLSP provide valuable insights to the community and shall ease the decision of whether or not to use GLSP for future tool development projects.

Keywords: Conceptual Modeling, Analytical models, Systematics, Machine Learning algorithms, Bibliographies, Semantics, Machine Learning
Astract: Conceptual models are essential in Software and Information Systems Engineering to meet many purposes since they explicitly represent the subject domains. Machine Learning (ML) approaches have recently been used in conceptual modeling to realize, among others, intelligent modeling assistance, model transformation, and metamodel classification. These works en-code models in various ways, making the encoded models suitable for applying ML algorithms. The encodings capture the models' structure and/or semantics, making this information available to the ML model during training. Therefore, the choice of the encoding for any ML-driven task is crucial for the ML model to learn the relevant contextual information. In this paper, we report findings from a systematic literature review which yields insights into the current research in machine learning for conceptual modeling (ML4CM). The review focuses on the various encodings used in existing ML4CM solutions and provides insights into i) which are the information sources, ii) how is the conceptual model's structure and/or semantics encoded, iii) why is the model encoded, i.e., for which conceptual modeling task and, iv) which ML algorithms are applied. The results aim to structure the state of the art in encoding conceptual models for ML.

Keywords: Digital Twin, Knowledge engineering, Model driven engineering, circular economy, sustainability
Astract: The next generation of engineered systems ought to be more sustainable. In this context, Digital Twins play a crucial role as key enablers of sustainability ambitions in systems engineering. However, as a specific class of engineered systems, Digital Twins themselves must adopt sustainability principles to avoid defeating their purpose in fostering sustainability. In this proposal, we focus on the technical sustainability of Digital Twins, enabled by their evolution. We propose an initial taxonomy we believe will support systematic Digital Twin evolution mecha-nisms and draw links to similar taxonomies of Physical Twins.

Keywords: Artificial intelligence, Conceptual modeling, Enterprise modeling, Machine learning, Model-driven engineering
Astract: Artificial Intelligence (AI) continuously paves its way into even the most traditional business domains. This particularly applies to data-driven AI, like machine learning (ML). Several data-driven approaches like CRISP-DM and KKD exist that help develop and engineer new ML-enhanced solutions. A new breed of approaches, often called canvas-driven or visual ideation approaches, extend the scope by a perspective on the business value an ML-enhanced solution shall enable. In this paper, we reflect on two recent ML projects. We show that the data-driven and canvas-driven approaches cover only some necessary information for developing and operating ML-enhanced solutions. Consequently, we propose to put ML into an enterprise context for which we sketch a first framework and spark the role enterprise modeling can play.