Scientific Data Management research group

​​The challenges that the research group is working on include:

• Devise Knowledge graphs able to encode the meaning and context of scientific data and contain knowledge about provenance, privacy, quality, and uncertainty.
• Domain-specific ontologies and link discovery techniques are capable of promoting the interoperability of heterogeneous and large scientific datasets in a scalable manner.
• Integration methods for heterogeneous and extensive scientific data sources, for example, legacy data, structured and unstructured data, as well as static data and continuous data streams.
• Storage and distribution of large-scale scientific data and knowledge graphs.
• Access control methods to enforce privacy regulations for sensitive data. 
• Query engines for distributed scientific knowledge graphs.
• Data analysis and methods of knowledge discovery via scientific knowledge graphs.