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: Artificial intelligence, DMN, Enterprise modeling, Explainable AI, Machine learning, Model-driven engineering
Astract: The Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach.

Keywords: Archi, ArchiMate, Enterprise architecture, Knowledge graph, Modeling tool
Astract: This paper presents the EAKG Toolkit that entails a new Knowledge Graph-based representation of enterprise architecture (EA) models and further enables reasoning on EA knowledge. Our developed EAKG Toolkit is unique in the sense that it i) transforms ArchiMate models into a KG representation – the Enterprise Architecture Knowledge Graph (EAKG), ii) visualizes the EAKG for interactive exploration, and iii) extends the EAKG with additional nodes and edges to visually represent detected EA smells.

Keywords:
Astract: The digital transformation is having a huge impact on many sectors of the economy. Recently, it has gained momentum in the construction industry and in tunnelling in particular. This article explains the challenges associated with the digital transformation of tunnelling and how they are being addressed by a current research project. The project is an inter-university and interdisciplinary project with the aim of advancing digitalisation in tunnelling. The article discusses the topic using various use cases that demonstrate solutions to the current challenges.

Keywords: Conceptual Modeling, Graphical Language Server Protocol, Integrated Development Environment, Language Server Protocol, Modeling tools, Software Engineering
Astract: With the introduction of the Language Server Protocol (LSP), a fundamental shift has been observed in the development of language editing support for Integrated Development Environments (IDEs), such as VS Code, the traditional Eclipse IDE, or Eclipse Theia. LSP establishes a uniform protocol that standardizes the communication between a language client (e. g., an IDE like Eclipse) and a language server (e. g., for a programming language like Java). The language client only needs to be able to interpret and understand the protocol instead of the specific programming language. Likewise, the language server can focus on language support and does not need to consider the specifics of a respective IDE. This reduces the complexity of realizing language support on different editors and IDEs and enables smooth transitions from one IDE to another. LSP is an open and community-driven protocol that has been developed within the realm of the VS Code community, initiated and driven by Microsoft. The generic concept and architectural pattern of LSP enables widespread applications that go far beyond the realization of editing support for programming languages. This paper provides an introduction to LSP, describes its evolution and core characteristics, and delineates its potential for revolutionizing not only the IDE market but also other software systems, such as modeling tools.

Keywords: Conceptual Modeling

Keywords: Graph Neural Networks, Ontology-Driven Conceptual models, Representation Learning
Astract: Conceptual Models (CMs) are essential for information systems engineering since they provide explicit and detailed representations of the subject domains at hand. Ontology-driven conceptual modeling (ODCM) languages provide primitives for articulating these domain notions based on the ontological categories put forth by upper-level (or foundational) ontologies. Many existing CMs have been created using ontologically-neutral languages (e.g., UML, ER). Connecting these models to ontological categories would provide better support for meaning negotiation, semantic interoperability, and complexity management. However, given the sheer size of this legacy base, manual stereotyping is a prohibitive task. This paper addresses this problem by proposing an approach based on Graph Neural Networks towards automating the task of stereotyping UML class diagrams with the meta-classes offered by the ODCM language OntoUML. Since these meta-classes (stereotypes) represent ontological distinctions put forth by a foundational ontology, this task is equivalent to ontological category prediction for these classes. To enable this approach, we propose a strategy for representing CM vector embeddings that preserve the model elements’ structure and ontological categorization. Finally, we present an evaluation that shows convincing learning of OntoUML model node embeddings used for OntoUML stereotype prediction.

Keywords: Model-Based Construction