Title: Semantic Data Interoperability in Practice: Lessons from Public Administration and Beyond

Abstract: Digital transformation in the public sector increasingly depends on the ability to exchange, interpret, and reuse data across organizational and technological boundaries. While the promise of interoperable and machine-actionable public data has been discussed for decades, practical implementation remains challenging. This keynote reflects on the Czech experience with semantic interoperability and Linked Open Data (LOD), examining obstacles encountered and approaches taken.
Beyond technical data exchange lies a closely related challenge: achieving a shared understanding of what data actually means, i.e. semantic interoperability. Although Semantic Web technologies are well established, their application in cross-border and cross-domain data exchange continues to raise new organizational and methodological challenges.
The second part of the keynote will focus on semantic data specifications in the context of the European Union and the Common European Data Spaces, addressing how such specifications can be reused consistently across national and domain-specific contexts. This will be illustrated through the example of DCAT-AP, the Interoperable Europe solution for metadata exchange.
The keynote will conclude with reflections on how the development and adoption of semantic interoperability solutions may be supported by AI, and conversely, how semantic technologies and structured knowledge may provide essential foundations for AI systems.

Object-Process Methodology (OPM, ISO 19450:2024): Redefining Systems Engineering in the AI Era

The rise of advanced artificial intelligence and Large Language Models (LLMs) introduces new challenges in systems engineering. Consequently, managing semantic alignment across multidisciplinary human-AI teams requires new approaches. Object-Process Methodology (OPM) addresses this complexity through a native bimodal framework that unifies graphical diagrams with structured text-based sentences.

Furthermore, OPM explicitly embeds teleology by modeling the system’s purpose and its beneficiary at the very beginning of system development. This initial model of purpose encapsulates the essence of the entire solution, which, although not yet realized, is already fully present in the mind of its creator. Consequently, further system development, with some methodological exaggeration, merely involves describing internal technical details through progressive and syntactically controlled decomposition into detailed sub-models. This precise focus on unified, multi-level decomposition remains unsupported by traditional modeling languages.

Moreover, OPM models act as formal conceptual structures that can be dynamically simulated and verified. This capability provides a significant advantage, as it allows domain experts and systems engineers to verify and validate the evolving model through visual inspection and simulation. Consequently, the human remains the final arbiter within this AI-supported systems engineering ecosystem.

In this light, OPM can be understood not simply as another modeling notation, but rather as a significant qualitative leap in systems thinking. For half a century, the traditional object-oriented paradigm and modeling languages like UML have subordinated dynamic behavior to static data structures by making processes (methods) the strict property of a single object class. In contrast, OPM is based on an entirely different modeling paradigm with deep philosophical foundations. This innovative approach establishes a symmetric ontology in which objects and processes exist as completely equal entities that mutually define each other through interconnected links.

Advancing Agility and Shared Understanding in Digital Ecosystem Design using Digital Twins: The Scene2Model Approach

The increasing complexity of digital ecosystems continues to challenge the development and extension of information systems. Managing change across multidisciplinary teams and enabling innovation at scale requires approaches that foster both agility and shared understanding. Design thinking plays a crucial role in this context, supporting agile ideation, prototyping, and testing through collaborative exploration.

However, traditional design thinking approaches are often constrained by synchronous collaboration, reliance on physical artifacts, and limited scalability. This tutorial introduces the Scene2Model approach developed within OMiLAB, which enables the transformation of design thinking artifacts into digital twins. These digital representations can be shared, refined, and semantically enriched across distributed environments, supporting reuse and integration with business assets.

By combining conceptual modeling with design thinking, Scene2Model bridges informal design artifacts and formal abstractions used in information systems development. The approach enhances co-creation, supports shared understanding, and facilitates the transition from early ideation to structured system design.

Title: The Great Convergence: Recommendation, Search, and Conversation in Real-World AI Systems

Abstract: For most of their history, recommendation, search, and conversational interfaces have been built as separate systems, with distinct architectures, evaluation methods, and engineering teams. This separation is rapidly disappearing. Embeddings, foundation-model backbones, and large language models are pushing these three paradigms toward a single underlying stack, in which a query, a click, a scroll, and a natural-language request are increasingly different entry points into the same retrieval-and-ranking machinery. This talk examines what such convergence means in practice for the applications we build, and the new challenges it brings, both theoretical and practical. On the theoretical side, unifying interaction-based and semantic signals raises hard questions about evaluation, cold-start behavior, bias and alignment. On the practical side, deploying these systems in the real world forces an uncomfortable trade-off: they must be not only accurate, but also fast and economically viable at the scale of hundreds of millions of users — a constraint that reshapes architectural decisions far more than benchmark leaderboards suggest. Drawing on experience from both academic research and the production systems behind Recombee, the talk explores how to scale converged recommendation–search–conversation systems while keeping latency and cost under control, and how to ensure user satisfaction and AI alignment across very different domains, from news and media, through e-commerce, to education. The central argument is that the next generation of information systems will be defined less by raw model capability and more by how thoughtfully we engineer the interaction between accuracy, affordability, and alignment.

Title: Towards controlled and sustainable AI in Model-Driven Development: Guard Rails based on Normalized Systems Theory

Abstract: Normalized Systems theory uses concepts such as stability from systems theory to study the evolvability of modular structures. Its focus is on eliminating so-called combinatorial effects from modular structures, which are ripple effects whose number of impacts is related to the size of the modular structure, which makes them highly undesirable from the point of view of controlled and sustainable evolution of for example, software architectures. To build Normalized Systems in practice, fine-grained skeletons of modular structures are generated and continuously re-generated over time by code generators implementing the principles of Normalized Systems Theory and Model-Driven Development. This approach is very well suited as a complement to the use of AI and LLMs for code generation, as the skeletons can be easily extended with micro-doses of AI-generated code, while at the same time
providing guard rails for the use of AI. These guard rails add traceability and control not only to the code generation process (which are currently seen as major limitations of LLM-based code generation), but also to the continuous re-generation of AI-generated software architectures over time.

Engineering Digital Sovereignty in AI-Enabled Information Systems: Principles, Architectures, and Emerging Research Directions

Goal and specific area of interest

Artificial Intelligence, cloud computing, and data-driven platforms are fundamentally transforming the way information systems are designed, deployed, and managed. At the same time, increasing dependence on global digital infrastructures has raised critical concerns regarding digital sovereignty,the ability of individuals, organizations, and nations to retain control over their data, technologies, AI systems, and digital infrastructure. This tutorial introduces the concept of Digital Sovereignty by Design, demonstrating how sovereignty principles can be integrated throughout the Information Systems Development (ISD) lifecycle. Rather than viewing sovereignty as solely a legal or policy issue, the tutorial presents it as an essential design principle for modern AI-enabled information systems.

Tutorial format

Part I: Digital Sovereignty: Why Information Systems Researchers Should Care (20 min)
Part II: Engineering Sovereignty into Information Systems(30 min)
Part III: Case Studies and Emerging Applications (25 min)
Part IV: Future Research Directions and Discussion (15 min)

Participants

The tutorial is intended for researchers in Information Systems, AI and Data Science, Software Engineers, PhD students, Government and industry practitioners

Value for Participants

Participants will:

• Understand the emerging concept of Digital Sovereignty and its relevance to Information Systems Development.
• Learn practical architectural principles for developing sovereignty-aware information systems.
• Discover how AI, cloud computing, and data governance influence system design decisions.
• Explore recent international case studies from smart cities, finance, Industry 5.0, and federated AI.
• Identify promising research directions and collaboration opportunities.

The tutorial provides both conceptual understanding and practical guidance that participants can immediately apply to research and system development.