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.

Keywords: ArchiMate, value co-creation
Astract: The design and engineering of collaborative networks and business ecosystems is a discipline that requires an outstanding and upfront attention of the value cogenerated among the parties involved in the business exchanges of these networks. Understanding this value co-creation is undoubtedly paramount, first to adequately sustain the design and the development of the information system that brings about this value, second, to support the communication between the information system designers, and third to allow discovering new co-creation opportunities among the networks companies. In that context, we proposed an abstract language (meta-model) that structures, and provides an explanatory semantics to, the co-creation of value between information system designers, allowing a better definition of the collaboration and of each one of the value propositions. The design of this language is achieved in the frame of the design science theory and accordingly follows an iterative improvement approach based on real case studies from practitioners. This chapter introduces the second iteration of the language based on a real case in a smart airport network.

Keywords: Conceptual Modeling
Astract: Fundamental decisions are made in the early phases of software development. The typical outcomes of these phases are models of different kinds, such as architectural models, data models, and process models. Automation support is required to efficiently and effectively handle large models and conduct continuous quality improvement processes. Thus, several approaches have been proposed that integrate modeling with Artificial Intelligence (AI) methods such as Genetic Algorithms (GAs), among others. These approaches, e.g., transform models to improve their quality by searching for good solutions within the potential solution space. In this chapter, we first review existing applications of AI methods to model-based software engineering problems. Subsequently, we show a representative use case of how a model-based software analysis and design problem can be solved using GAs. In particular, we focus on the well-known and challenging modularization problem: splitting an overarching, monolithic model into smaller modules. We present two encodings, the model-based and the transformation-based encoding, which are both applied for the modularization of Entity-Relationship (ER) diagrams. We further discuss how these encodings may be adapted to other structural models and conclude with an outlook on future research lines related to software modeling intelligence.

Keywords: Inclusive conceptual modeling, Inclusion, Diversity, Equity, Involvement, Belonging

Keywords: Digital Transformation
Astract: We posit that AI-Enabled Enterprise is the next phase of digital transformations seen so far. We will start by defining more precisely what we mean by digital transformation and argue that it’s important to ensure these transformations are well-coordinated. We posit this coordination should be seen as a continuous process and take a model-enabled perspective on it. In doing so, we will argue for the need to identify (and manage) so-called enterprise design dialogues, where enterprise models are positioned as a key artefact in support of these enterprise design dialogues. Before concluding, we also review some of the challenges and opportunities towards future research.

Keywords: Enterprise architecture, Historical analysis, Knowledge graph
Astract: 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.