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)
Incoming Non-self Citations Over Time
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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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Outgoing Citations (Sorted by Pagerank)
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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| 8,302 | Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories | 2014 | VLDB | 4.5435639e-05 |
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| 9,751 | Co-movement Pattern Mining from Videos | 2024 | VLDB | 4.2897489e-05 |
| 1,745 | Discovery of Convoys in Trajectory Databases | 2008 | VLDB | 0.00010702338 |