Abstract
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 introduce our novel approach of Query-driven Knowledge-Sharing Systems (QKSS). A QKSS extends a data management system with knowledge-sharing capabilities to facilitate user collaboration without altering data analysis workflows. Collective knowledge from the query log is extracted to support data source discovery and data integration. Knowledge is formalized to enable its sharing across data scientist teams.
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Wahl, A.M., Endler, G., Schwab, P.K., Herbst, S., Lenz, R. (2017). Query-Driven Knowledge-Sharing for Data Integration and Collaborative Data Science. In: Kirikova, M., et al. New Trends in Databases and Information Systems. ADBIS 2017. Communications in Computer and Information Science, vol 767. Springer, Cham. https://doi.org/10.1007/978-3-319-67162-8_8
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DOI: https://doi.org/10.1007/978-3-319-67162-8_8
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