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: Enterprise Modelling
Astract: This volume of the Lecture Notes in Business Information Processing series contains the proceedings of the 16th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling (PoEM), held in Vienna, Austria, during November 28th to December 1st, 2023. The PoEM working conference aims to improve the understanding of the practice of Enterprise Modeling (EM) by offering a forum for sharing experiences and knowledge between the academic community and practitioners from industry and the public sector. These proceedings include 12 full papers selected out of 34 full papers sent for peer review (35.3% acceptance rate). All submissions have been thoroughly reviewed in a single-blind process by three program committee members. The review process was led by the program committee chairs João Paulo A. Almeida and Monika Kaczmarek- Heß overseen by the general chairs Henderik A. Proper and Agnes Koschmider. The selected papers cover topical areas such as Enterprise Modeling and Artificial Intel- ligence, Enterprise Modeling and Emerging Architectures, Enterprise Modeling and Digital Transformation, Enterprise Modeling Tools and Approaches, etc. We would like to show our greatest appreciation to the submitting authors and the members of the program committee as well as additional reviewers for their hard work.

Keywords: Enterprise Modelling
Astract: For over twenty-five years the EDOC conference has been the primary annual event for disseminating and discussing the latest developments in enterprise computing. In addition to the main track, EDOC 2022 offered a forum, a demonstration track, and a doctoral consortium. It also hosted three workshops of interest to the community. All of these events were held in-person, together with the main conference in Bozen-Bolzano, Italy. The forum was introduced this year as a track within the main conference where authors were given a platform to present and discuss early-stage work. In this first edition, we accepted four forum papers for presentation and publication in this proceedings volume. The demonstration track offered a highly interactive outlet for researchers and prac- titioners to present prototypes and applications in the context of enterprise computing. This year, we accepted five tools for presentation, each of which was accompanied with a short paper published here.

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: Conceptual Modeling

Keywords: Collaborative modeling, GLSP, UML, bigUML, Workflow tool, Web modeling, Modeling tool, Visual Studio Live Share
Astract: The PC has become an integral part of our working environment. It supports us at school and university, at work and in our free time. The coronavirus pandemic has increased this dependency enormously. Many lessons have been held remotely, friends have communicated via video calls and many of us have worked completely from home. However, working from home also brings a number of difficulties and problems with it, for example how several people can work together efficiently on the same thing. This is where collaborative working starts.In the context of IT, collaborative work means that several people are working on a document at the same time. This applies to textual documents, as well as all other kinds of documents. This diploma thesis is concentrating on diagrams, i.e. graphical documents that interact with GLSP (Graphical Language Server Platform). GLSP offers a platform, which provides a protocol, to develop modeling tools for diagrams. This work is intended to extend the GLSP protocol, so it will provide collaborative real-time modeling on diagrams.Analyzing existing collaborative editors should provide a good base upon to define clear requirements for the solution. The raised questions deal with how GLSP can be extended in order to use it for collaborative purposes. Furthermore, the investigation should lay the foundation for creating a good user experience within a collaborative session. A prototype based on the Workflow tool aims to implement the developed concept and provide a first insight into collaborative modeling with GLSP. An evaluation should also demonstrate that it is easily possible to apply the prototype to other modeling languages. Finally, a comprehensive real-time test will check the solution for performance, user-friendliness and reliability.

Keywords: model engineering, UML, sequence diagram, GLSP, web modeling, modeling tool, bigUML
Astract: The sequence diagram (SD) plays a significant role in software development and business organization. It defines processes and systems interactions with a precise temporal semantic. The temporal semantics bring about additional complexity, making SD modeling tools bothersome. Available SD modeling tools struggle to provide a functional yet user-friendly palette of interaction behaviors. This difficulty results in divergent implementations and modeling behaviors, causing widespread poor usability. The reliance of tools on long-established technologies further hinders more efficient tool functions and design. Switching to modern web technologies could unlock more advanced modeling interactions. Doing so as part of an open-source project allows the adoption of implementations in other diagrams and enables future adaptions and improvements of the tool. This thesis analyses the most noted SD modeling tools and derives a systematic overview of their functionalities and implemented behaviors. Based on this, an SD modeling tool with its respective interactions and editing behaviors is conceptualized to fulfill the semantic requirements and ensure usability. The concept is realized as an artifact developed on the Graphical Language Server Platform (GLSP), adding the support for SDs to the open-source UML modeling editor bigUML currently in development. The tool features essential SD modeling behaviors with the addition of specialized functionalities, rendering the modeling process more reactive and dynamic. It combines robust modeling functionalities and the convenience of web-based features. The implementation of the concepts is assessed against the conceptualized requirements, and the resulting artifact undergoes evaluation through a defined modeling process, enabling a comparison with existing solutions.