Crowdsourcing Analytics with CrowdCur
Summary: CrowdCur enables crowdsourcing analytics for admins, requesters, and workers via worker curation and completion-based task curation. OLAP-style analytics by worker or task type; admins tune, requesters compare platforms, workers locate tasks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Mohammadreza Esfandiari (New Jersey Institute of Technology)
- 2. Kavan Bharat Patel (New Jersey Institute of Technology)
- 3. Sihem Amer-Yahia (CNRS; University of Grenoble)
- 4. Senjuti Basu Roy (New Jersey Institute of Technology)
BibTeX Citation
@inproceedings{esfandiari_sigmod18,
title = {{Crowdsourcing Analytics with CrowdCur}},
author = {Esfandiari, Mohammadreza and Patel, Kavan Bharat and Amer-Yahia, Sihem and Roy, Senjuti Basu},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3193563},
url = {https://dl.acm.org/doi/10.1145/3183713.3193563},
year = {2018}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,136 | Combining User Interaction, Speculative Query Execution and Sampling in the DICE System | 2014 | VLDB | 9.1126926e-05 |
| 10,029 | Human Factors in Crowdsourcing | 2016 | VLDB | 5.1741314e-05 |
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