Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3022198.3026327acmconferencesArticle/Chapter ViewAbstractPublication PagescscwConference Proceedingsconference-collections
poster

We Can Query More than We Can Tell: Facilitating Collaboration Through Query-Driven Knowledge-Sharing

Published: 25 February 2017 Publication History

Abstract

We introduce Query-driven Knowledge-Sharing Systems (QKSS), which extend data management systems with knowledge-sharing capabilities to facilitate collaboration among different teams of data scientists. Relevant tacit knowledge about data sources is extracted from SQL query logs and externalized to support data source discovery and data integration. By studying this collaborative knowledge, data scientists are enabled to formulate effective analytical queries over unfamiliar data sources.

References

[1]
M. S. Ackerman and others. 2013. Sharing Knowledge and Expertise: The CSCW View of Knowledge Management. CSCW 22, 4 (2013).
[2]
G. Allen and J. Parsons. 2010. Is Query Reuse Potentially Harmful? ISR 21, 1 (2010).
[3]
M. Eirinaki and others. 2014. QueRIE: Collaborative Database Exploration. KDE 26, 7 (2014).
[4]
N. Khoussainova and others. 2010. SnipSuggest: Context-aware Autocompletion for SQL. PVLDB 4, 1 (2010).
[5]
I. Nonaka and N. Konno. 1998. The concept of "ba": Building a foundation for knowledge creation. CMR 40, 3 (1998).
[6]
M. Polanyi. 2009. The Tacit Dimension. University of Chicago Press, Chicago.

Index Terms

  1. We Can Query More than We Can Tell: Facilitating Collaboration Through Query-Driven Knowledge-Sharing

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    CSCW '17 Companion: Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
    February 2017
    472 pages
    ISBN:9781450346887
    DOI:10.1145/3022198
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 25 February 2017

    Check for updates

    Author Tags

    1. collaborative data science
    2. data integration
    3. data source discovery
    4. knowledge sharing

    Qualifiers

    • Poster

    Conference

    CSCW '17
    Sponsor:
    CSCW '17: Computer Supported Cooperative Work and Social Computing
    February 25 - March 1, 2017
    Oregon, Portland, USA

    Acceptance Rates

    CSCW '17 Companion Paper Acceptance Rate 183 of 530 submissions, 35%;
    Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

    Upcoming Conference

    CSCW '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 148
      Total Downloads
    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 07 Nov 2024

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media