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k/2-hop: Fast Mining of Convoy Patterns With Effective Pruning

Summary: Fast exact sequential convoy mining via k/2-hop, parameter-free. Key-timestamp processing and aggressive pruning shrink candidates, delivering orders-of-magnitude speedups and scalable performance on large datasets where prior methods falter. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12017
Venue
VLDB
Year
2019
Pagerank
4.7316373e-05
Overall Rank
7,432 | 48.30%
DOI
10.14778/3329772.3329773

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
9,751 Co-movement Pattern Mining from Videos 2024 VLDB 4.2897489e-05
10,576 Mining Platoon Patterns from Traffic Videos 2025 VLDB 4.1945683e-05
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Showing 2 of 2 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
4,622 A General and Parallel Platform for Mining Co-Movement Patterns over Large-scale Trajectories 2017 VLDB 6.0416152e-05
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