  In larger organizations, multiple teams of data scientists have to integrate
data from heterogeneous data sources as preparation for data analysis tasks.
Writing effective analytical queries requires data scientists to have in-depth
knowledge of the existence, semantics, and usage context of data sources. Once
gathered, such knowledge is informally shared within a specific team of data
scientists, but usually is neither formalized nor shared with other teams.
Potential synergies remain unused. We therefore introduce a novel approach
which extends data management systems with additional knowledge-sharing
capabilities to facilitate user collaboration without altering established data
analysis workflows. Relevant collective knowledge from the query log is
extracted to support data source discovery and incremental data integration.
Extracted knowledge is formalized and provided at query time.
