Enterprise Design, Operations, and Computing. EDOC 2025 Workshops

.- Forum.

.- Designing an Observable Human-centric Agentic AI System Architecture for Decision-Making.

.- Enhancing business dashboards with Explanatory analytics.

.- ProA: A Bottom-Up Process Architecture Approach for Automated Generation of Process Maps Based on BPMN Process Models.

.- Impact and Implications of Generative AI for Enterprise Architects in Agile Environments: A Systematic Literature Review.

.- Reuse and guidance for generative AI solution development and implementation: knowledge management perspective.

.- Enhancing Process Model Comprehension through Animations: A comparative study of tokens vs. highlighting.

.- Process Visualizations for Object-centric Process Mining: An Evaluation Taxonomy.

.- Visualising and Monitoring Object-centric Processes with Complex Object Interdependencies.

.- DMN-Guided Prompting: A Framework for Controlling LLM Behavior.

.- Evaluating LLM-Based Process Explanations under Progressive Behavioral-Input Reduction.

.- Design Principles for Data Trustee Governance: Application and Validation in a Geospatial Data Space.

.- FreeCAT - Towards a Framework for Resilience and Evolution by Code Analysis Tools.

.- Investigating the Role of FAIR Data Principles for Data Mesh Architecture.

.- Doctoral Consortium.

.- Report from the Doctoral Consortium Chairs.

.- A Method for Model-driven Engineering of IoT-based Digital Twins.

.- Adapting Conventional Business Process Management to Digital Process Innovation: A Comparative Study of China and Western Europe.

.- Industry 5.0 Performance Indicators for Sustainable Business Practices..

.- Formal Foundations for Enterprise Ontology: Ontologically Grounded Enterprise Information Systems.

.- Towards a Knowledge Management Treatment for Design Thinking with the Help of Knowledge Graphs.

.- Towards a Reference Ontology and -Architecture for Public-Private Learning Communities.

.- EA4AI - International Workshop on Enterprise Architecture for Augmented Intelligence.

.- Report from the EA4AI 2025 Workshop Chairs.

.- LLM4BPMN: A Prompting Approach with LLMs for BPMN Model Generation.

.- Designing a Predictive Digital Twin Architecture for Smart Bridge Maintenance.

.- CLAIR: Generating On-Demand Low-Code Application Documentation through Knowledge Graph and LLM-based Multi-Agent System.

.- iRESEARCH - 3rd Workshop on Empirical Methodologies for Research in EA and Service/Data-oriented Computing.

.- Report from the iRESEARCH 2025 Workshop Chairs.

.- Machine Learning-based Enhancement of Access Control in ERP Systems Using Real-world Data.

.- From Process Mining to Simulation: A Systematic Review of Data Quality Issues in Event Logs for Business Process Simulation.

.- A Design Method for Intelligence Amplification.

.- Optimization of care pathways with the use of process mining and text mining techniques: combining PM2 and CRISP-ML methodologies.

.- SoEA4EE'2025 - 16th Workshop on Service oriented Enterprise Architecture for Enterprise Engineering.

.- Report from the SoEA4EE'2025 Workshop Chairs.

.- Dematerializing Promissory Notes: A Service-Oriented Blockchain Architecture and Web Platform for Secure Financial Document Management.

.- Investigating Future Viability in Enterprise Architecture from the Practitioners' Perspective.

.- A Decade of Challenges: A Practitioners Exploratory Study of Microservices Operational Stability.

.- Discovery Rules Related to Acquiring Stakeholders for the Fractal Enterprise Model.

.- Tool Presentations.

.- Report from the Tools and Demos Chairs.

.- FEM Viewer Version 2: How Much Functionality Should Be Given to the Experts?.

.- DISME: A DEMO Based Model-Driven Low-Code/No-Code Platform.

.- KPI-driven Visualizations for Root Cause Analysis in Manufacturing Processes.

.- Atlas: the Enterprise Architecture & Cartography Tool.

.- Journal-first Papers.

.- Decentralized Proof-of-Location systems for trust, scalability, and privacy in digital societies.

.- How Can Cryptography Secure Online Assessments Against Academic Dishonesty?.

.- Building Trust in Predictive Analytics: A Review of ML Explainability and Interpretability.