Keywords: Construction Management, Tunnelling, Process mining, Documentation, Data analysis
Astract: Conventional tunnel construction often relies on manual methods of construction process analysis, using tools such as paper-based cycle diagrams or spreadsheets, which lack immediate updates and capabilities, limiting performance evaluation, communication, and decision-making. As a result, moving to a fully digital process incorporating business intelligence capabilities can deliver benefits by improving data-driven decision-making, operational efficiency and resource allocation. This paper presents a case study using construction documentation to evaluate the applicability of data and process analytics in conventional tunnelling. We also present a novel approach to visualising and analysing construction sequence deviations. The study demonstrates how data and process analysis can be utilised to evaluate the activity sequences, the duration of single activities, advance rates, and general project performance. By adhering to established industry standards, this research examines the practical implementation of data analysis methods in operational tunnelling environments, contributing to the development of integrated digital workflows.

Keywords: Digital Twin Engineering, Enterprise Architecture, Sustainable Real Estate Renovation
Astract: In the Architecture, Engineering, Construction & Operations (AECO) sector, Digital Twins (DTs) serve as transformative tools to integrate sustainability into renovation decision-making by providing dynamic, data-driven representations of physical assets. They enable stakeholders to optimise processes, improve resource efficiency, and ensure compliance with evolving regulations. However, the implementation of DTs in this context remains challenged by fragmented data ecosystems, insufficient regulatory integration, and limited consideration of sustainability goals. Enterprise Architecture (EA) offers a structured approach to overcoming these challenges by aligning technical, organisational, and sustainability-related objectives. To advance this, we define two key research objectives: (1) assessing current EA approaches for integrating DTs, and (2) developing sustainability-oriented EA modelling concepts and design patterns. Our aim is to position DTs as comprehensive decision-support tools for sustainability, enabling stakeholders to achieve better-informed decisions while addressing regulatory compliance, resource efficiency, and life cycle impacts.

Keywords: Enterprise Architecture, Architectural Coordination, Digital Twins
Astract: Digital Twins (DTs) carry the promise of supporting better decision-making, monitoring, and learning in relation to the twinned entity, by integrating novel technologies, including digital models, symbolic and sub-symbolic artificial intelligence, as well as advanced optimisation, simulation, and visualisation techniques. However, delivering such a promise requires considerable investments, which can only valorise in the long run, as DTs tend to be ‘data hungry’, in need of ample sensors, actuators and serious computing power. Yet, most current approaches to DT development focus on isolated scenarios, which not only limits the understanding of the value of DTs, but also their broader implications. The introduction of DTs, generally, also entails a wider digital transformation in an (inter-)organisational context, while such transformations need to be properly managed. We also observe that, since DTs are fundamentally a class of (highly advanced) information systems, this inevitably makes them an integral part of an enterprise’s broader (inter-organisational) portfolio of information systems. In line with this, we argue that, in order to (also) improve the socio-economical sustainability of DT solutions, their development, deployment and evolution need to be subject to architectural coordination within the broader frame of enterprise architecture management (EAM). From this perspective, we discuss some potential directions of research in (enterprise) architectural coordination of DT development, in order to help address some crucial challenges of socio-economically sustainable development and evolution of DTs as part of a broader portfolio of information systems.

Keywords: conceptual modeling, encoding, machine learning
Astract: The integration of Conceptual Modeling (CM) and Machine Learning (ML) has given rise to a growing research field known as Machine Learning for Conceptual Modeling (ML4CM), where ML techniques are applied to support modeling tasks such as classifica-tion, completion, or repair. A crucial factor in these applications is the transformation of conceptual models into ML-compatible representations, called encodings. A wide variety of encoding strategies exist that draw on different information sources within conceptual models, depending on the specific use case. However, existing ML4CM studies tend to treat encodings as fixed and focus predominantly on tuning ML algorithms or hyperparameters. Consequently, encoding strategies and their internal configuration options receive limited scrutiny during evaluation, making it difficult for researchers and practitioners to select and adapt optimal encodings for specific tasks.This thesis addresses this gap by developing and evaluating a set of configurable semantic encodings for conceptual models. Specifically, it investigates how semantic information (e.g. names, types, contextualrelationships) within models can be systematically extracted and transformed into ML-compatible representations. The work adopts the Design Science Research methodology and extends the CM2ML framework with an ArchiMate parser and four semantic encoders: Bag-of-Words (BoW), Term Frequency (TF), Embeddings,and Triples. Each encoder captures distinct semantic aspects and supports extensive configurability to enable experimentation and task-specific adaptation. Furthermore, all encodings can be interactively visualized within the framework, offering real-time insight into parameter effects and traceability to link encoded features back to their source model elements.To evaluate the proposed encodings, the thesis combines a qualitative comparison based on defined criteria with a quantitative assessment through two representative ML tasks.The first task, dummy classification, employs TF encodings to distinguish dummy views from valid ones and explores the impact of common NLP parameters and weighting schemes. The second task, node classification, aims to predict element types based on local context, using triple encodings enriched with word embeddings for element names and one-hot vectors for types. The results demonstrate the suitability of the encodings for specific ML4CM tasks and that certain encoding configurations can have a substantial influence on model performance.

Keywords: Process Patterns, Teaching Modelling

Keywords: Conceptual modeling, Data modeling, ER, Genetic algorithms, Graph neural networks, Model Modularization
Astract: Due to the crucial role conceptual models play in explicitly representing a subject domain, it is imperative that they are comprehensible and maintainable by humans. Modularization, i.e., decomposing an overarching, monolith model into smaller modules, is an established technique to make the model comprehensible and maintainable. Genetic Algorithms (GA) have been applied to modularize conceptual models by formulating desired structural model characteristics as multiple objectives. Recently, Graph Neural Networks (GNN)-based methods have shown promising performance in graph processing tasks, including graph clustering but outside the conceptual modeling domain. In this paper, we present a novel approach for GNN-based conceptual model modularization and comparatively analyze our approach against an existing multi-objective GA-based one. Furthermore, we provide a comparative analysis of our novel GNN model against two existing GNN-based graph clustering approaches. We investigate the dependence of the quality of the modularized solutions on the model size. We discuss the comparative results of our novel GNN-based approach and the existing GA-based approach to derive future research lines. Furthermore, our results show, that our proposed GNN-based modularization outperforms the existing GNN-based graph clustering approaches and provides a suitable alternative compared to the GA-based modularization.

Keywords: API classification, BERT, GPT, OpenAPI Specification
Astract: Application Programming Interfaces (APIs) enable interaction, integration, and interoperability among applications and services, contributing to their adoption and proliferation. However, discovering APIs has relied on manual, time-consuming, costly processes that jeopardize their reuse potential and accentuate the need for effective API retrieval mechanisms. Leveraging the OpenAPI Specification as a basis, this paper presents an exploratory study that combines BERT and GPT machine learning models to propose a novel API classifier. Our investigation explored the zero-shot learning capabilities of GPT-4 and GPT-3.5 using relevant terms extracted from API descriptions using BERT. The evaluation of our approach on two datasets comprising 940 API descriptions sourced from public repositories yielded an F1-score of 100% in the small dataset (17 APIs) and 39.1% in the large dataset (923 APIs). These results surpass state-of-the-art on the small dataset with an impressive 29-point improvement. The large dataset showed GPT can suggest labels not in the provided list. Manual analysis revealed that GPT’s suggested labels fit the API intent better in 18 out of 20 cases, highlighting its potential for unknown classes and mismatch detection. This emphasizes the need to improve dataset quality and availability for API research. Our findings show the potential of automated API retrieval and open avenues for future research.

Keywords: Conceptual Modeling, Sustainability

Keywords: Conceptual modeling, Domain modeling, Generative AI, LLM, UML
Astract: The information systems engineering community is increasingly exploring the use of Large Language Models (LLMs) for a variety of tasks, including domain modeling, business process modeling, software modeling, and systems modeling. However, most existing research remains exploratory and lacks a systematic approach to analyzing the impact of prompt content on model quality. This paper seeks to fill this gap by investigating how different levels of description granularity (whole text vs. paragraph-by-paragraph) and modeling strategies (model-based vs. list-based) affect the quality of LLM-generated domain models. Specifically, we conducted an experiment with two state-of-the-art LLMs (GPT-4o and Llama-3.1-70b-versatile) on tasks involving use case and class modeling. Our results reveal challenges that extend beyond the chosen granularity, strategy, and LLM, emphasizing the importance of human modelers not only in crafting effective prompts but also in identifying and addressing critical aspects of LLM-generated models that require refinement and correction.

Keywords: Agility, Business Process Management, Conversational Agents, LLM, Social media
Astract: The traditional approaches in dynamic and collaborative environments that use Business Process Management (BPM) methodologies usually lack the ability to adapt to real-time changes in case of heavy human involvement in repetitive processes. The agility of social BPM is, however, still limited because of a lack of context-sensitive tool support. This paper proposes a mapping framework that leverages conversational AI agents on a social media platform to enhance BPM agility. AI-driven conversational agents are mapped to the respective phases of the BPM lifecycle to provide real-time guidance, recommendations, and context-sensitive feedback. The agents’ collaborative features enable inclusive co-construction, interactive task execution, and continuous monitoring of the processes. That allows dynamic adaptation of the processes in case of changes so that tasks remain aligned with the users’ needs and contextual demands. This framework is developed through an exploratory approach that integrates literature review, deductive design, and use case-based evaluation. This framework could bridge gaps in the current BPM practices by integrating BPM, AI, and social media, thereby offering a new model for agile and collaborative business process management.