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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)

Paper ID
h706c220826992fa3
Venue
VLDB
Year
2017
Pagerank
4.9793485e-05
Overall Rank
12,314 | 17.21%
DOI
10.14778/3025111.3025118

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

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
5,061 CDB: A Crowd-Powered Database System 2018 VLDB 6.2912543e-05
5,570 Efficient Algorithms for Crowd-Aided Categorization 2020 VLDB 6.0811968e-05
7,042 Human-in-the-loop Outlier Detection 2020 SIGMOD 5.6142998e-05
7,500 Crowdsourced Data Management: Overview and Challenges 2017 SIGMOD 5.5083793e-05
9,583 CDB: Optimizing Queries with Crowd-Based Selections and Joins 2017 SIGMOD 5.1571823e-05
12,212 A Rating-Ranking Method for Crowdsourced Top-k Computation 2018 SIGMOD 4.9793485e-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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