CDB: Optimizing Queries with Crowd-Based Selections and Joins
Summary: Graph-based query model for crowd-powered selections and joins enables fine-grained, per-tuple optimization beyond coarse tree orders. Unified multi-goal optimization (cost, latency, quality) under a single framework, with AMT/CrowdFlower/ChinaCrowd deployments and benchmarks showing gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Guoliang Li (Tsinghua University)
- 2. Chengliang Chai (Tsinghua University)
- 3. Ju Fan (Renmin University of China)
- 4. Xueping Weng (Tsinghua University)
- 5. Jian Li (Tsinghua University)
- 6. Yudian Zheng (University of Hong Kong)
- 7. Yuanbing Li (Tsinghua University)
- 8. Xiang Yu (Tsinghua University)
- 9. Xiaohang Zhang (Tsinghua University)
- 10. Haitao Yuan (Tsinghua University)
BibTeX Citation
@inproceedings{li_sigmod17,
title = {{CDB: Optimizing Queries with Crowd-Based Selections and Joins}},
author = {Li, Guoliang and Chai, Chengliang and Fan, Ju and Weng, Xueping and Li, Jian and Zheng, Yudian and Li, Yuanbing and Yu, Xiang and Zhang, Xiaohang and Yuan, Haitao},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3064036},
url = {https://dl.acm.org/doi/10.1145/3035918.3064036},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,051 | CDB: A Crowd-Powered Database System | 2018 | VLDB | 6.2863221e-05 |
| 5,830 | Domain Adaptation for Deep Entity Resolution | 2022 | SIGMOD | 5.9627079e-05 |
| 6,404 | Human-in-the-loop Data Integration | 2017 | VLDB | 5.7797097e-05 |
| 6,654 | Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning | 2023 | VLDB | 5.7038474e-05 |
| 6,948 | Interactive Graph Search | 2019 | SIGMOD | 5.6210529e-05 |
| 7,064 | Human-in-the-loop Outlier Detection | 2020 | SIGMOD | 5.5930864e-05 |
| 7,074 | Cost-Effective Data Annotation using Game-Based Crowdsourcing | 2019 | VLDB | 5.5915502e-05 |
| 10,594 | Weighted Set Multi-Cover on Bounded Universe and Applications in Package Recommendation | 2026 | SIGMOD | 4.9605343e-05 |
| 12,261 | A Rating-Ranking Method for Crowdsourced Top-k Computation | 2018 | SIGMOD | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 28 of 28 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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|---|---|---|---|---|
| 1 | 1,861 | CrowdDB: Query Processing with the VLDB Crowd | 2011 | VLDB |
| 2 | 5,478 | Crowdsourcing Applications and Platforms: A Data Management Perspective | 2011 | VLDB |
| 3 | 5,627 | CrowdQ: Crowdsourced Query Understanding | 2013 | CIDR |
| 4 | 10,771 | Sample-based Distinct Cardinality Estimation for Multiple Attributes in Multi-Dataset Queries | 2026 | VLDB |
| 5 | 10,448 | Faster Relational Algorithms Using Geometric Data Structures | 2026 | PODS |
| 6 | 873 | Leveraging Transitive Relations for Crowdsourced Joins | 2013 | SIGMOD |
| 7 | 7,805 | Pushing the Boundaries of Crowd-enabled Databases with Query-driven Schema Expansion | 2012 | VLDB |
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| 9 | 92 | CrowdDB: Answering Queries with Crowdsourcing | 2011 | SIGMOD |
| 10 | 5,051 | CDB: A Crowd-Powered Database System | 2018 | VLDB |