gMission: A General Spatial Crowdsourcing Platform
Summary: gMission presents a general spatial crowdsourcing platform that leverages mobile sensing for location-based data collection. It integrates geographic sensing, worker detection, and task recommendation in a scalable architecture to tackle incentives, task assignment, data quality, and result aggregation, illustrated by case analyses. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhao Chen (Hong Kong University of Science and Technology)
- 2. Rui Fu (Hong Kong University of Science and Technology)
- 3. Ziyuan Zhao (Hong Kong University of Science and Technology)
- 4. Peng Cheng (Hong Kong University of Science and Technology)
- 5. Caleb Chen Cao (Hong Kong University of Science and Technology)
- 6. Zheng Liu (Hong Kong University of Science and Technology)
- 7. Yongxin Tong (Hong Kong University of Science and Technology)
- 8. Leihao Xia (Hong Kong University of Science and Technology)
- 9. Lei Chen (Hong Kong University of Science and Technology)
- 10. Chen Jason Zhang (Hong Kong University of Science and Technology)
BibTeX Citation
@article{chen_vldb14,
title = {{gMission: A General Spatial Crowdsourcing Platform}},
author = {Chen, Zhao and Fu, Rui and Zhao, Ziyuan and Cheng, Peng and Cao, Caleb Chen and Liu, Zheng and Tong, Yongxin and Xia, Leihao and Chen, Lei and Zhang, Chen Jason},
journal = {PVLDB},
series = {{VLDB} '14},
volume = {7},
number = {13},
pages = {1629--1632},
doi = {10.14778/2733004.2733047},
url = {https://doi.org/10.14778/2733004.2733047},
year = {2014}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,661 | Online Minimum Matching in Real-Time Spatial Data: Experiments and Analysis | 2016 | VLDB | 6.5792798e-05 |
| 7,357 | Crowdsourced Data Management: Overview and Challenges | 2017 | SIGMOD | 5.6346837e-05 |
| 7,417 | An Experimental Evaluation of Task Assignment in Spatial Crowdsourcing | 2018 | VLDB | 5.6236299e-05 |
| 8,747 | Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers | 2015 | VLDB | 5.3766157e-05 |
| 9,597 | Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach | 2018 | SIGMOD | 5.2494989e-05 |
| 11,956 | Worker Recommendation for Crowdsourced Q&A Services: A Triple-Factor Aware Approach | 2018 | VLDB | 5.093636e-05 |
| 12,017 | Spatial Crowdsourcing: Challenges, Techniques, and Applications | 2017 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 743 | So Who Won? Dynamic Max Discovery with the Crowd | 2012 | SIGMOD | 0.00014421358 |
| 4,153 | Whom to Ask? Jury Selection for Decision Making Tasks on Micro-blog Services | 2012 | VLDB | 6.8676997e-05 |
| 4,539 | Reducing Uncertainty of Schema Matching via Crowdsourcing | 2013 | VLDB | 6.6403822e-05 |
| 5,143 | An Online Cost Sensitive Decision-Making Method in Crowdsourcing Systems | 2013 | SIGMOD | 6.3476753e-05 |
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| 1 | 8,747 | Reliable Diversity-Based Spatial Crowdsourcing by Moving Workers | 2015 | VLDB |
| 2 | 11,602 | AMRAS: A Visual Analysis System for Spatial Crowdsourcing | 2022 | VLDB |
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| 4 | 11,501 | Privacy-preserving Cooperative Online Matching over Spatial Crowdsourcing Platforms | 2023 | VLDB |
| 5 | 8,788 | Managing General and Individual Knowledge in Crowd Mining Applications | 2015 | CIDR |
| 6 | 11,925 | Crowdsourcing Analytics with CrowdCur | 2018 | SIGMOD |
| 7 | 2,780 | A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing | 2014 | VLDB |
| 8 | 12,068 | Collaborative Crowdsourcing with Crowd4U | 2016 | VLDB |
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| 10 | 7,417 | An Experimental Evaluation of Task Assignment in Spatial Crowdsourcing | 2018 | VLDB |