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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)

Paper ID
5876
Venue
SIGMOD
Year
2020
Pagerank
4.4324982e-05
Overall Rank
8,865 | 38.33%
DOI
10.1145/3318464.3384703

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,751 Co-movement Pattern Mining from Videos 2024 VLDB 4.2897489e-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
1,745 Discovery of Convoys in Trajectory Databases 2008 VLDB 0.00010702338
2,192 DITA: Distributed In-Memory Trajectory Analytics 2018 SIGMOD 9.3185895e-05
7,037 Real-time Distributed Co-Movement Pattern Detection on Streaming Trajectories 2019 VLDB 4.8548122e-05
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