Imagine you run a business that pulls information from the web — prices, medical records, product data — and builds software around it. Every time the data changes, your software breaks. ALIGNED built tools that let your data and your software evolve together, automatically catching errors and keeping everything consistent. Think of it like spell-check, but for entire databases — it flags bad data before it causes expensive problems downstream.
Companies that build applications on top of web data spend enormous effort keeping their software and data in sync. When data sources change format or quality drops, applications break — and finding the problem manually is slow and expensive. There is no standard, lightweight way to automatically test data quality and evolve software alongside the data it depends on.
The project delivered working prototypes including: a Semantic Booster that auto-generates code transformations from system specifications, a Model Catalogue Tool integrated with Eclipse IDE for managing data models, and an automated data testing and verification tool (based on RDFUnit) that generates and runs test cases for data consistency, completeness, and coverage — integrated into standard Maven build processes.
If you are a legal information publisher dealing with constantly changing regulations across multiple jurisdictions — this project developed an automated data testing tool that checks your datasets for consistency, completeness, and coverage before they reach your customers. Wolters Kluwer, one of the 3 industry partners, used these tools in their production Linked Data systems.
If you are a healthcare IT provider struggling to keep patient data systems updated as medical standards and data formats evolve — this project built a Semantic Booster that auto-generates software transformations from system specifications. Oxford used these methods to transform NHS systems, showing the approach works at national scale.
If you are a data company spending too much time manually checking whether scraped or integrated web data is accurate and complete — this project delivered an automated verification tool integrated with Maven build processes. It generates test cases automatically from your data specifications, catching quality issues before they propagate through your pipeline.
The tools were developed as open-source prototypes with a total EU investment of EUR 3,999,934 across 7 partners. Integration costs would depend on your existing tech stack — the data testing tool plugs into Maven build processes, and the Model Catalogue integrates with Eclipse IDE, so teams already using these will have lower adoption costs.
The tools were validated with real-world partners handling large datasets: Wolters Kluwer processes legal information at scale, and Oxford applied the methods to NHS systems. The automated test generation approach is designed to scale — it generates test cases from specifications rather than requiring manual test writing.
The project committed to open-source tool releases and engaged standards bodies including OMG, W3C, and ISO for technology transfer. Based on available project data, the core tools (Semantic Booster, Model Catalogue, RDFUnit-based testing) were developed as open-source, meaning licensing costs should be minimal or zero.
All 4 demo deliverables reached Phase 3 (final prototype stage) by project end in January 2018. They are functional prototypes tested with real partners, not just lab experiments. However, as the project closed in 2018, you should verify current maintenance status and community activity.
The tools were specifically designed for integration: the Model Catalogue works within Eclipse IDE, and the data testing tool integrates with Maven build processes. This means they slot into standard Java/enterprise development workflows rather than requiring a separate toolchain.
The project included 19 total deliverables covering scientific publications, training programs, and standards engagement with W3C, OMG, and ISO. The consortium of 7 partners across 5 countries built a research community, but ongoing commercial support would likely need to come through the industry partners or a service agreement.
The 7-partner consortium across 5 countries (Austria, Germany, Ireland, Poland, UK) has a strong 43% industry ratio with 3 industry partners alongside 4 universities. This is a well-balanced team: Trinity College Dublin coordinated, with world-class research from Oxford and Leipzig (co-creators of DBpedia). On the business side, Wolters Kluwer brings real production data systems, and Semantic Web Company is a recognized leader in enterprise Linked Data. The 1 SME in the group adds agility. For a potential business buyer, the presence of Wolters Kluwer as a validation partner is the strongest signal — they would not participate unless the tools addressed real production needs.
Trinity College Dublin coordinated this project. The research team can be reached through the university's Computer Science department.
Want to know if ALIGNED's automated data quality tools fit your data pipeline? We can arrange a technical briefing with the research team and assess integration feasibility for your specific setup.
