INFORMCLUB
Research needs a credible technical idea and an executable programme. InformClub supports concept development, proposal work, architecture, partner interfaces and technical delivery.
Technical scope, programme-fit assessment, proposal contribution, partner work packages, architecture, integration and execution planning.
Current collaboration through EU-GPT includes technical execution planning for AI R&D. Knowledge retrieval, controlled workflows and model evaluation connect this work with earlier research experience.
From construction and manufacturing to media, learning and enterprise information, the recurring challenge is to connect knowledge, software and people. These domains show how that experience supports business innovation today.
Construction quality, complex manufacturing and remanufacturing: connect operational information with workflows and decisions.
ACCEPT · ARUM · PREMANUS
Neo4j/Cypher · microservices · OWL · cloud integration
Model meaning explicitly, map different data structures and make information reusable across applications.
STASIS · OPDM · CHIME · eUser
RDF/OWL · SPARQL · Jena · reasoning · machine learning
Bring content, user context and analytics together across distributed platforms and APIs.
SAM · OMELETTE
Python/Jupyter · KNIME · SQL · REST · Node.js
Represent what people know and what they are trying to achieve, then adapt guidance and information.
INTUITEL · MONAP · CHIME
Learner models · Bayesian methods · semantic reasoning · agents
Turn integration requirements into architecture, prototypes and components that can work together.
NEXOF-RA · SOA4ALL · CREATE
UML · Java/Spring · REST/HATEOAS · service composition
Our team participated in these research and engineering activities through previous professional and academic roles. Explore the project domains, technologies and contributions, with public references.
Technical work on the information lake, data integration and distributed services.
Relevant to connected operational data, graph-backed information systems and field-workflow integration.
Deputy coordinator, technical manager and responsibility for data analytics.
Relevant to contextual information delivery, analytics and technical coordination across partners.
Responsibility for the system-integration work package, including semantic data transformation and distributed service integration.
Relevant to industrial integration, coordinated agents and architectures that connect planning with existing systems.
Proposal writing, negotiation and project setup, with data and service integration work.
Relevant to personalised learning, guided workflows and turning research concepts into integration plans.
Proposal contribution, project setup, technology selection and software architecture.
Relevant to lifecycle data, decision support and planning a technical path from proposal to delivery.
Project-team work in service integration; co-author of the OMELETTE approach publication.
Relevant to API composition, reusable components and interfaces that let people combine services.
Technical lead for the mobile demonstrator subproject integrating REST services into one interface.
Relevant to service discovery, API integration and building usable products on connected data.
Software architecture, prototype development and project-management work; co-author of the reference-architecture publication.
Relevant to architecture decisions, interoperable platforms and testing designs through practical prototypes.
Technical lead for software integration and senior development, combining ontology engineering, user-centred design and machine learning.
Relevant to knowledge models, schema alignment and giving information consistent meaning across applications.
Research and development of agent-based exchange, user-model services and ontologies.
Relevant to shared context, semantic service discovery and coordination across applications.
Research engineering, architecture and prototypes, with co-authored publications.
Relevant to contextual knowledge access, personalisation and modular information systems.
Analysis, design and implementation, with research publications on learning management and tutoring-system design.
Relevant to adaptive learning platforms, guided practice and software that responds to individual progress.
CREATE brought service and data integration into configurable industrial workflows. OPDM applied semantic transformation to product information in e-commerce. Team participation in these activities included Java, REST, JavaScript/Node.js, widgets and OWL/RDF-based product-data mapping.
Our team has contributed to research on information access, semantic interoperability, adaptive systems and AI. These selected publications connect that work with business and R&D questions today, pairing short original excerpts with practical interpretation.
2015
From the original research · English excerpt
“early detection of unexpected situations followed by rapid mitigation actions”
Operations need to detect exceptions, combine information from existing systems and help people choose a response. The ARUM work connects service architecture with adaptive production management. It informs our approach to software integration, decision support and evaluating coordinated agents within real operating constraints.
For R&D: define the integration architecture, responsibilities and scenarios that a pilot must demonstrate.
2012
From the original research · English excerpt
“so that knowledge about semantic languages is not required.”
A business user should be able to express a need without learning the technical vocabulary of the underlying system. This research explored matching user queries with semantically described services. Today, that experience informs natural-language interfaces, semantic retrieval and discovery of relevant information or tools.
For R&D: compare matching methods against representative questions, ambiguity and domain vocabulary before choosing an approach.
2004
From the original research · English excerpt
“offering each individual user (or user group) the information they need.”
More information does not automatically help a customer or employee complete a task. Role, goal and context should shape what an application presents and how it guides the next step. This principle informs useful assistants, knowledge portals and adaptive workflows—supported by modular architecture and deliberate choices about user context.
For R&D: model the roles, goals and tasks, then test whether adaptation helps people complete the intended work.
2004
From the original research · English excerpt
“enable a shared understanding of concepts throughout the system”
Systems can exchange data while still disagreeing about what it means. Explicit domain concepts, relationships and mappings provide a foundation for consistent search, knowledge graphs and integration. This is relevant when an AI workflow must work across several sources or applications and preserve the meaning of business information.
For R&D: make the domain model, mappings and component interfaces explicit, then test them with realistic information flows.
2001
From the original research · English excerpt
“different levels of adaptation (to the specific student) depending on the learning stage.”
AI in education has long involved more than delivering content: modelling what a person knows, selecting useful practice and changing the support as learning progresses. The MONAP work provides an early foundation for this approach. Today it informs learning platforms, employee onboarding, guided practice and assistants that respond to a user’s level of understanding.
For R&D: define the learner model, feedback and adaptation rules, and evaluate learning progress alongside usability and task completion.
Start with one initiative. Tell us what you want to build or improve, and we can agree a useful first step.