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Towards Globally Optimal Crowdsourcing Quality Management: The Uniform Worker Setting

Summary: Under a uniform worker model, the paper devises algorithms that provably achieve the global optimum of the maximum-likelihood estimates for task answers and worker quality (yes/no and rating tasks). It prunes the mapping space to preserve optimality, characterizes the problem's complexity, and often outperforms EM-based estimates. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5144
Venue
SIGMOD
Year
2016
Pagerank
4.8085946e-05
Overall Rank
7,178 | 50.07%
DOI
10.1145/2882903.2882953

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

Rank Cited Paper Year Venue Pagerank
263 CrowdER: Crowdsourcing Entity Resolution 2012 VLDB 0.00029862413
1,164 CrowdScreen: Algorithms for Filtering Data with Humans 2012 SIGMOD 0.00013564823
1,410 Entity Resolution with Iterative Blocking 2009 SIGMOD 0.00012127555
1,841 Crowdsourcing Algorithms for Entity Resolution 2014 VLDB 0.00010348858
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