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The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing

Summary: Two-class crowdsourcing model (experts vs. naive) with a threshold error framework for evaluating accuracy-cost tradeoffs. Proposes a max-finding algorithm achieving a constant-factor approximation with expert and naive workers, and tight upper/lower bounds, validated on CrowdFlower data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h775f055c43781c4f
Venue
SIGMOD
Year
2015
Pagerank
4.9793485e-05
Overall Rank
12,394 | 16.67%
DOI
10.1145/2723372.2723722

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Authors

BibTeX Citation

@inproceedings{anagnostopoulos_sigmod15,
        title = {{The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing}},
        author = {Anagnostopoulos, Aris and Becchetti, Luca and Fazzone, Adriano and Mele, Ida and Riondato, Matteo},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2723372.2723722},
        url = {https://dl.acm.org/doi/10.1145/2723372.2723722},
        year = {2015}
}

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Showing 7 of 7 cited papers.

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

Rank Cited Paper Year Venue Pagerank
92 CrowdDB: Answering Queries with Crowdsourcing 2011 SIGMOD 0.00034672523
257 Crowdsourced Databases: Query Processing with People 2011 CIDR 0.00022962347
266 Human-powered Sorts and Joins 2012 VLDB 0.00022739124
749 So Who Won? Dynamic Max Discovery with the Crowd 2012 SIGMOD 0.00014265279
987 CrowdScreen: Algorithms for Filtering Data with Humans 2012 SIGMOD 0.00012660627
3,838 Optimal Crowd-Powered Rating and Filtering Algorithms 2014 VLDB 6.9929907e-05
4,220 Whom to Ask? Jury Selection for Decision Making Tasks on Micro-blog Services 2012 VLDB 6.7252203e-05
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