The increasing availability of data from diverse sources presents new opportunities for inclusive and data-driven modeling, particularly in the area of human behavior analysis. Process discovery, as a data-driven technique of Process Mining (PM), can be used to extract behavioral and workflow models from event logs. However, this technique requires well-structured event logs that accurately reflect actual processes. In many cases, discovery approaches rely on single-source datasets and rarely integrate heterogeneous data and contextual information. This lack of integration limits the ability to extract deeper insights into human behavior and hinders comprehensive and interpretable modeling. To address this, we propose a methodological approach called Human Behavior Monitoring with Unified Log and Contextualized Process Discovery (HB-UniContex). HB-UniContex consolidates data from multiple sources, including smart sensing systems, multimedia data streams and human-generated data sources, into a unified event log that supports context-aware process discovery and detailed visualization. HB-UniContex enables a clear extraction and interpretation of human behavior in relation to contextual information. We demonstrate the applicability of this method through a case study in an Ambient Assisted Living (AAL) setting, analyzing human behavior models and examining the relationship between mood states and daily activities. Our findings reveal specific behavior patterns associated with different mood states, providing precise insights into how an individual’s daily mood correlates with certain daily activities.
Enterprises are increasingly challenged to make sense of proliferating digital traces in ways that are semantically coherent, organizationally actionable, and institutionally aligned. This research introduces CoEDiT–a commitment-driven enterprise digital twin–conceived as a reflexive modeling infrastructure that integrates conceptual abstraction, semantic technologies, and empirical data analytics. Grounded in Action Design Research, CoEDiT operationalizes a dialectical loop wherein social commitments serve as ontologically grounded constructs that link strategic intention to operational execution. Applied to air navigation services, the approach demonstrates how latent coordination structures can be surfaced and organizational learning sustained through the semantic reinterpretation of transactional data. Beyond its practical instantiation, CoEDiT contributes to current debates in Information Systems and Business Informatics by proposing a socio-technical modeling grammar that supports institutional sensing, strategic seizing, and organizational transformation. In doing so, it recasts enterprise modeling not as a representational end, but as a dynamic capability for reflexive adaptation in reindustrialized contexts.
Digital Twins (DTs) carry the promise of improved decision-making about, as well as monitoring and understanding of, the twinned entity. This makes them an attractive instrument to support the, often complex and multi-faceted, decision-making processes germane to urban planning. DTs require considerable technological investments, as they tend to be data-hungry and computing-intensive. Business-wise, such investments are only meaningful if they really add value to the intended decision-making processes. However, most current DT development approaches primarily focus on the technological potential of DTs within the limited scope of isolated business scenarios, and rarely address trade-offs between costs and benefits towards the business case, let alone the broader implications for IT/IS portfolio management. These broader considerations are crucial in urban planning contexts, which typically involve a broad ecosystem of parties, complex decision-making challenges, and pre-existing technological landscapes. Drawing on the discipline of Enterprise Architecture Management (EAM), this paper argues that architectural coordination of DT development initiatives would enable more effective valorisation of DTs potential, and more effective management of DT-related technology within a broader technological landscape. To this end, the paper discusses the vision for and an initial sketch of a specialisation of EAM for DT development.
Conceptual modeling, particularly with Entity-Relationship Diagrams (ERDs), is traditionally a manual and complex task in data engineering. This paper presents a method and tool for automating ERD generation from semi-structured data using Large Language Models (LLMs). The approach follows an iterative cycle, data ingestion, prompt design and execution, and results evaluation, with human-in-the-loop refinement to improve accuracy through expert feedback. Validation was performed on two datasets, assessing model quality via coverage, referential integrity, and Third Normal Form (3NF) compliance. Results showed high scores (0.90 and 0.93), confirming the tool’s effectiveness and the need for expert input to resolve complex modeling issues. This work offers an LLM-assisted solution to assist in complex data engineering tasks, while acknowledging the continued importance of expert oversight.
Modeling is a core task in enterprise systems engineering. The use of graphical modeling editors, however, remains cumbersome in general and poses a significant challenge for users with disabilities. Natural language processing (NLP) and intent recognition are at the forefront of making many technologies more accessible and intuitive by allowing users to engage using natural language. This paper presents a natural language interface (NLI) for speech-based UML model interaction that leverages state-of-the-art NLP technologies to enable speech-based modeling. We provide a workflow for the creation of NLIs for modeling editors, a proof-of-concept integration of this approach into the bigUML open-source modeling editor, and an empirical evaluation that shows promising results in intent recognition, the effectiveness of model creation, and usability. Thereby, this paper makes significant contributions towards more natural, inclusive, and accessible modeling.
