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Query-Driven Knowledge-Sharing for Data Integration and Collaborative Data Science

  • Andreas M. WahlEmail author
  • Gregor Endler
  • Peter K. Schwab
  • Sebastian Herbst
  • Richard Lenz
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 767)

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andreas M. Wahl
    • 1
    Email author
  • Gregor Endler
    • 1
  • Peter K. Schwab
    • 1
  • Sebastian Herbst
    • 1
  • Richard Lenz
    • 1
  1. 1.Computer Science 6 (Data Management)FAU Erlangen-NürnbergErlangenGermany

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