Keywords: Enterprise Modelling
Astract: In the Western world, digital has become the new normal, both in our daily lives and at our work. Additionally, Western countries have seen a transition from a goods-oriented economy to a services-oriented economy. Whereas, in the recent past, it was already the case that change was the only constant, these intertwined, and mutually amplifying, trends even further increase the pace of change. As a result, enterprises are confronted with a need to transform (continuously) accordingly. During any enterprise transformation, coordination among the key stakeholders and the projects that drive the transformations is essential. A shared understanding, agreement, and commitment are needed on (1) what the overall mission/vision of the enterprise is, (2) the current affairs of the enterprise and any ongoing changes, (3) the current affairs of the context of the enterprise, and (4) what (given the latter) the ideal future affairs of the enterprise are. Models, and ultimately enterprise (architecture) modeling languages and framework, are generally considered as an effective way to support such (informed) coordination. In the past, different frameworks and languages have been developed to this end, including the ArchiMate language. The latter has evolved to become a widely accepted industry standard. The objective of this chapter is threefold: (1) we intend to illustrate some of the key challenges which the digital transformation, and the two intertwined trends that drive it, puts on enterprise (architecture) modeling languages, (2) assess to what extent ArchiMate meets these challenges, and (3) draft the outline of a next-generation enterprise (architecture) modeling language (framework) that may be more suited to meet the challenges of these trends.

Keywords: Data set, Enterprise architecture, Enterprise modeling, FAIR, Machine learning
Astract: The conceptual modeling community and its subdivisions of enterprise modeling are increasingly investigating the potentials of applying artificial intelligence, in particular machine learning (ML), to tasks like model creation, model analysis, and model processing. A prerequisite—and currently a limiting factor for the community—to conduct research involving ML is the scarcity of openly available models of adequate quality and quantity. With the paper at hand, we aim to tackle this limitation by introducing an EA ModelSet, i.e., a curated and FAIR repository of enterprise architecture models that can be used by the community. We report on our efforts in building this data set and elaborate on the possibilities of conducting ML-based modeling research with it. We hope this paper sparks a community effort toward the development of a FAIR, large model set that enables ML research with conceptual models.

Keywords: Deployment, Flexibility, GLSP, Language Server Protocol, LSP, Modeling tool, Tool development, Web modeling
Astract: In the past decade, the modeling community has produced many feature-rich modeling editors and tool prototypes not only for modeling standards but particularly also for many domain-specific languages. More recently, however, web-based modeling tools have started to become increasingly popular in the industry for visualizing and editing models adhering to such languages. This new generation of modeling tools is built with web technologies and offers much more flexibility when it comes to their user experience, accessibility, reuse, and deployment options. One of the technologies behind this new generation of tools is the Graphical Language Server Platform (GLSP), an open-source client-server framework hosted under the Eclipse foundation, which allows tool developers to build modern diagram editors for modeling tools that run in the browser or can be easily integrated into IDEs such as Eclipse, VS Code, or Theia. In this paper, we describe our vision for more flexible modeling tools which is based on our experiences from developing several traditional and web-based modeling tools in an industrial and academic context. With that, we aim at sparking a new line of research and innovation in the modeling community for modeling tool development practices and to explore opportunities, advantages, and limitations of web-based modeling tools, as well as bridge the gap between scientific tool prototypes and industrial tools being used in practice.

Keywords: Accessibility, Conceptual Modeling, Disability, Modeling tools, Systematic Literature Review, Tool Review
Astract: The reports on Disability by the World Health Organization show that the number of people with disabilities is increasing. Consequently, accessibility should play an essential role in information systems engineering research. While software and web engineering research acknowledge this need by providing, e.g., web accessibility guidelines and testing frameworks, we show in this paper, based on a systematic review of the literature and current modeling tools, that accessibility is, so far, a blind spot in conceptual modeling research. With the paper at hand, we aim to identify current research gaps and delineate a vision toward more inclusive, i.e., disability-aware conceptual modeling. One key finding relates to a gap in research and tool support concerning physical disabilities. Based on these results, we further present the first modeling tool that can be used keyboard-only, thereby including users with physical disabilities to engage in conceptual modeling.

Keywords: Generic Framework, Genetic Algorithm, Modularization
Astract: Conceptual models need to be comprehensible and maintainable by humans to exploit their full value in faithfully representing a subject domain. Modularization, i.e. breaking down the monolithic model into smaller, comprehensible chunks has proven very valuable to maintain this value even for very large models. The quality of modularization however often depends on application-specific requirements, the domain, and the modeling language. A well-defined generic modularizing framework applicable to different modeling languages and requirements is lacking. In this paper, we present a customizable and generic multi-objective conceptual models modularization framework. The multi-objective aspect supports addressing heterogeneous requirements while the framework’s genericity supports modularization for arbitrary modeling languages and its customizability is provided by adopting the modularization configuration up to the level of using user-defined heuristics. Our approach applies genetic algorithms to search for a set of optimal solutions. In this paper, we present the details of our Generic Genetic Modularization Framework with a case study to show i) the feasibility of our approach by modularizing models from multiple modeling languages, ii) the customizability by using different objectives for the modularization quality, and, finally, iii) a comparative performance evaluation of our approach on a dataset of ER and ECore models.

Keywords: Conceptual modeling, Human–computer interaction, Information visualization, Modeling tools, Notation, Taxonomy
Astract: Conceptual modeling is integral to computer science research and is widely adopted in industrial practices, e.g., business process and enterprise architecture management. Providing adequate and usable modeling tools is necessary to adopt modeling languages efficiently. Meta-modeling platforms provide a rich and mature set of functionalities for realizing state-of-the-art modeling tools. These tools, albeit their stability and rich set of features, often lack a modern look and feel considering (i) how they visualize the models, and (ii) how modelers interact with the models. Current web technologies enable much richer, advanced opportunities for visualizing and interacting with conceptual models. However, a structured and comprehensive overview of possible information visualization and interaction techniques linked to conceptual models and modeling tools must be established. This paper aims to fill this gap by presenting an extended taxonomy of advanced information visualization and interaction in conceptual modeling. We present a generic taxonomy that is afterward contextualized within the specific domain of conceptual modeling. The taxonomy serves orientation in the vast developing field of information visualization and interaction and hopefully sparks innovation if future modeling tool development.

Keywords: Digital transformation, Documentation, Production, Tunneling, Software, Time measurement, Recording
Astract: The digitalization of Architecture, Engineering, & Construction (AEC), specifically the tunneling sector, often lags behind other sectors. Today's invoicing process of tunneling projects frequently involves many manual and paper-based tasks, which are error-prone and time-consuming. Accordingly, replacing this with a fully digital process, from the tunnel face to the project owner, bears high economic potential. This article proposes an integrated digital documentation system for tunnel construction management and invoicing. A month-long case study with data from a real-life construction site is conducted to test the system's suitability. The managed data are exported to commonly used enterprise resource planning (ERP) systems for invoicing and compared to actual invoicing data. The case study has shown that the proposed approach is practical and helped streamline and improve the construction documentation processes in conventional tunnel construction. The proposed solution represents an incremental contribution to the digital transformation of the AEC sector.

Keywords: tunneling, data capture, invoicing, digitalization, documentation

Keywords: Developing Economies, e-Government, e-Government Interoperability, Enterprise Architecture
Astract: The growth and uptake of e-government in developing economies is still affected by the interoperability challenge, which can be perceived as an orchestration of several issues that imply the existence of gaps in methods used for e-government planning and implementation. To a great extent, various counterparts in developed economies have succeeded in addressing the method-related gaps by developing e-government enterprise architectures, as blueprints for guiding e-government initiatives in a holistic and manageable way. However, existing e-government enterprise architectures are country-specific to appropriately serve their intended purpose, while enterprise architecture frameworks or methods are generic to accommodate several enterprise contexts. The latter do not directly accommodate the unique peculiarities of e-government efforts. Thus, a detailed method is lacking that can be adapted by developing economies to develop e-government enterprise architectures that fit their contexts. To address the gap, this article presents research that adopted a Design Science approach to develop an e-Government Enterprise Architecture Framework (EGEAF), as an explicit method for guiding the design of e-government enterprise architectures in a developing economy. EGEAF was designed by extending the Architecture Development Method of The Open Group Architecture Framework (TOGAF ADM) to address requirements for developing interoperable e-government solutions in a developing economy. EGEAF was evaluated using two scenarios in the Ugandan context, and findings indicate that it is feasible; its design is understandable to enable its adoption and extension to accommodate requirements for developing interoperable e-government solutions in other developing economies.

Keywords: Conceptual Modeling, Artificial Intelligence, Systematic Mapping Study
Astract: In conceptual modeling (CM), humans apply abstraction to represent excerpts of reality for means of understanding and communication, and processing by machines. Artificial Intelligence (AI) is applied to vast amounts of data to automatically identify patterns or classify entities. While CM produces comprehensible and explicit knowledge representations, the outcome of AI algorithms often lacks these qualities while being able to extract knowledge from large and unstructured representations. Recently, a trend toward intertwining CM and AI emerged. This systematic mapping study shows how this interdisciplinary research field is structured, which mutual benefits are gained by the intertwining, and future research directions.