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CrowdQ: Crowdsourced Query Understanding

Summary: Shifts hybrid human–machine focus from data curation to query understanding, using crowdsourcing combined with query-log mining and NLP to infer query structure and entity relationships. Produces reusable query templates to answer classes of questions rather than isolated Q/A. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
189
Venue
CIDR
Year
2013
Pagerank
6.2054029e-05
Overall Rank
5,474 | 62.45%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{demartini_cidr13,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '13},
        title = {{CrowdQ: Crowdsourced Query Understanding}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Demartini, Gianluca and Trushkowsky, Beth and Kraska, Tim and Franklin, Michael J.},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
7,788 A Natural Language Interface for Querying General and Individual Knowledge 2015 VLDB 5.5446256e-05
9,346 DataSift: A Crowd-Powered Search Toolkit 2014 SIGMOD 5.2844452e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
90 CrowdDB: Answering Queries with Crowdsourcing 2011 SIGMOD 0.00034951786
91 WebTables: Exploring the Power of Tables on the Web 2008 VLDB 0.00034838835
251 Crowdsourced Databases: Query Processing with People 2011 CIDR 0.00023261113
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