The research focus in data management covers six directions:

  • FAIR (Findability, Accessibility, Interoperability and Reusability) metadata: Solutions to add a semantic layer to the data management framework, which is linked to the metadata of the underlying data. Their main purpose is to harmonize different data and metadata schemas as well as different vocabularies and ontologies. Thus, both data and metadata gain explicitly semantic meaning.
  • Data in context: Methods and tools that repeatedly reuse a common knowledge base, contexts for data enrichment and thus improve data quality. For example, enriching a company’s data with a context from external sources such as news and social media deliver additional information about factors that influence its success. Later on, the exploration of data in context allows for the identification of external factors, that can be used to guide future decisions.
  • Data fabric: Methodologies and tools for development “fabric” for data integration and linking. Data fabrics produce federated data and allow for the translation of the existing data models into semantic knowledge models such as ontologies and taxonomies. They could be implemented using knowledge graphs in which all rules for the meaningful and dynamic linking of business objects are stored. Thus, the advantages of Data Lakes and Data Warehouses are complemented with the advanced linking methods of Semantic Graph Technologies.
  • Explicit semantics: Methods and tools that make the semantics of data explicit, which in turn becomes accessible, machine-readable and portable.
  • Store and manage data as knowledge: Exploration and application of knowledge graphs as a powerful knowledge management system.
  • Data quality: Methods and tools that validate data consistency and provide repair mechanisms on it as well as perform quality assurance or sanity check after enrichment, so that the quality and completeness can be assessed.