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December: A Declarative Tool for Crowd Member Selection

Summary: December is a declarative tool for crowd member selection using Member-QL to express profile, history, similarity, and task relevance. It provides semantically aware similarity and expertise in novel, efficient algorithms for holistic crowdsourcing. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11451
Venue
VLDB
Year
2016
Pagerank
5.093636e-05
Overall Rank
12,065 | 17.23%
DOI
10.14778/3007263.3007290

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Authors

BibTeX Citation

@article{amsterdamer_vldb16,
        title = {{December: A Declarative Tool for Crowd Member Selection}},
        author = {Amsterdamer, Yael and Milo, Tova and Somech, Amit and Youngmann, Brit},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {13},
        doi = {10.14778/3007263.3007290},
        url = {https://doi.org/10.14778/3007263.3007290},
        year = {2016}
}

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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
3,262 iCrowd: An Adaptive Crowdsourcing Framework 2015 SIGMOD 7.5846052e-05
6,311 Worker Skill Estimation in Team-Based Tasks 2015 VLDB 5.9175619e-05
7,081 OASSIS: Query Driven Crowd Mining 2014 SIGMOD 5.7086247e-05
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