In many applications, integration of heterogeneous data is a major challenge. Our research group develops scalable data integration solutions for small and big data in different areas (life science healthcare, industry). A powerful meta data management approach can extract meta data from various sources, enrich those with semantic annotations and integrate them via semi-automatic matching.
Additionally, our data integration approach is based on an incremental and interactive process. Data sources will first be filled into a data lake, retaining their original structure. The data lake provides a common access method for semi-structured data. Afterwards, step-wise data integration is performed with a focus on the demands of a particular application. We bypass the huge efforts demanded by a "global" data integration inherent in many data warehouse approaches. Instead, application-oriented integration is put into the hands of users via powerful and friendly user interfaces.
These methods are currently applied and refined in the following research projects:
- charMant – Development of a data management approach and its implementation for recording and using product-related machine and production process data with minimal effort
- HUMIT – Human-centered Support of Incrementally-interactive Data integration using the example of high-throughput processes in life sciences
- Industrial Data Space – Creating a safe data space for sovereign management of shared data among companies of different sectors and sizes
- Medical Data Space – The Medical Data Space is a trustworthy shared space designed for health care and translational research