Everybody is talking about artificial intelligence, but to be able to deliver high-quality results, AI models need a pre-processed database, that is, an organized collection of structured datasets with a shared objective (Kaufmann & Meier 2023). The more complex the source data, the more we need Big Data applications. They allow us to effectively process large data volumes in various formats.
To this end, research and development (R&D) into databases and big data aims to develop new solutions, e.g., through information extraction, automated data quality, knowledge graphs and agentic engineering - boosted by in-memory processing, hardware concurrency and linearized algorithms. This is how you get a handle on data. With a clean set of data, we can extract, process, and provide knowledge, enabling humans and AI agents alike to make data-informed decisions.
We build software prototypes from demos to proof-of-concepts to MVPs; we test them under real-life conditions, assess their performance, and optimize them. We help our business partners to address their data-related problems and support our research partners in answering transdisciplinary research questions. With our findings, we contribute to advancing the state of the art of data science.
Our areas of research:
- Data Integration: Linking information from different sources
- Information extraction: Processing of unstructured data and documents
- Big data: Performant parallel and linear scalable algorithms and systems
- Graph databases: Knowledge graphs and network analyses
- Research data management: Presenting results according to FAIR principles
Do you have a specific data challenge? Talk to us! Let’s discuss how we can build a bespoke solution for you through e.g., a workshop, proof-of-concept, or R&D project.
