CoMing: A Real-time Co-Movement Mining System for Streaming Trajectories
Summary: CoMing enables real-time co-movement pattern mining on streaming trajectories; uses ICPE’s distributed framework for scalable detection. Demonstration emphasizes visualization and interaction, with traffic-monitoring analytics for researchers. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ziquan Fang (Zhejiang University)
- 2. Yunjun Gao (Alibaba; Zhejiang University)
- 3. Lu Pan (Zhejiang University)
- 4. Lu Chen (Aalborg University)
- 5. Xiaoye Miao (Zhejiang University)
- 6. Christian S. Jensen (Aalborg University)
BibTeX Citation
@inproceedings{fang_sigmod20,
title = {{CoMing: A Real-time Co-Movement Mining System for Streaming Trajectories}},
author = {Fang, Ziquan and Gao, Yunjun and Pan, Lu and Chen, Lu and Miao, Xiaoye and Jensen, Christian S.},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3384703},
url = {https://dl.acm.org/doi/10.1145/3318464.3384703},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,081 | Co-movement Pattern Mining from Videos | 2024 | VLDB | 5.0830849e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,942 | Discovery of Convoys in Trajectory Databases | 2008 | VLDB | 9.3285345e-05 |
| 2,196 | DITA: Distributed In-Memory Trajectory Analytics | 2018 | SIGMOD | 8.8780862e-05 |
| 7,026 | Real-time Distributed Co-Movement Pattern Detection on Streaming Trajectories | 2019 | VLDB | 5.6180637e-05 |
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|---|---|---|---|---|
| 1 | 1,942 | Discovery of Convoys in Trajectory Databases | 2008 | VLDB |
| 2 | 12,566 | Mining and Linking Patterns across Live Data Streams and Stream Archives | 2013 | VLDB |
| 3 | 7,600 | k/2-hop: Fast Mining of Convoy Patterns With Effective Pruning | 2019 | VLDB |
| 4 | 12,313 | Efficient Mining of Regional Movement Patterns in Semantic Trajectories | 2017 | VLDB |
| 5 | 12,481 | MoveMine 2.0: Mining Object Relationships from Movement Data | 2014 | VLDB |
| 6 | 8,799 | MoveMine: Mining Moving Object Databases | 2010 | SIGMOD |
| 7 | 7,026 | Real-time Distributed Co-Movement Pattern Detection on Streaming Trajectories | 2019 | VLDB |
| 8 | 11,247 | Mining Platoon Patterns from Traffic Videos | 2025 | VLDB |
| 9 | 3,696 | A General and Parallel Platform for Mining Co-Movement Patterns over Large-scale Trajectories | 2017 | VLDB |
| 10 | 10,081 | Co-movement Pattern Mining from Videos | 2024 | VLDB |