DOCS: Domain-Aware Crowdsourcing System
Summary: DOCS models worker quality as domain-dependent rather than task-independent, deriving task/worker domains from knowledge bases. It combines domain-aware truth inference with online worker assignment, improving crowdsourcing accuracy over prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yudian Zheng (University of Hong Kong)
- 2. Guoliang Li (Tsinghua University)
- 3. Reynold Cheng (University of Hong Kong)
BibTeX Citation
@article{zheng_vldb17,
title = {{DOCS: Domain-Aware Crowdsourcing System}},
author = {Zheng, Yudian and Li, Guoliang and Cheng, Reynold},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {4},
pages = {361},
doi = {10.14778/3025111.3025118},
url = {https://doi.org/10.14778/3025111.3025118},
year = {2017}
}
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,188 | CDB: A Crowd-Powered Database System | 2018 | VLDB | 6.3111292e-05 |
| 5,842 | Efficient Algorithms for Crowd-Aided Categorization | 2020 | VLDB | 6.0526311e-05 |
| 6,933 | Human-in-the-loop Outlier Detection | 2020 | SIGMOD | 5.7191408e-05 |
| 7,392 | Crowdsourced Data Management: Overview and Challenges | 2017 | SIGMOD | 5.6111211e-05 |
| 9,442 | CDB: Optimizing Queries with Crowd-Based Selections and Joins | 2017 | SIGMOD | 5.2534872e-05 |
| 11,967 | A Rating-Ranking Method for Crowdsourced Top-k Computation | 2018 | SIGMOD | 5.0723324e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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