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)
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Authors
- 1. Yael Amsterdamer (Bar-Ilan University)
- 2. Tova Milo (Tel Aviv University)
- 3. Amit Somech (Tel Aviv University)
- 4. Brit Youngmann (Tel Aviv University)
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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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 |
|---|---|---|---|---|
| 3,197 | iCrowd: An Adaptive Crowdsourcing Framework | 2015 | SIGMOD | 7.5455503e-05 |
| 6,439 | Worker Skill Estimation in Team-Based Tasks | 2015 | VLDB | 5.7847877e-05 |
| 7,228 | OASSIS: Query Driven Crowd Mining | 2014 | SIGMOD | 5.5805857e-05 |
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