k/2-hop: Fast Mining of Convoy Patterns With Effective Pruning
Summary: k/2-hop is an exact, parameter-free sequential convoy miner that inspects selected key timestamps and prunes objects unable to form k-step convoys. It avoids data-dependent tuning and achieves orders-of-magnitude speedups over prior sequential and parallel methods. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Faisal Orakzai (Université Libre de Bruxelles)
- 2. Toon Calders (University of Antwerp)
- 3. Torben Bach Pedersen (Aalborg University)
BibTeX Citation
@article{orakzai_vldb19,
title = {{k/2-hop: Fast Mining of Convoy Patterns With Effective Pruning}},
author = {Orakzai, Faisal and Calders, Toon and Pedersen, Torben Bach},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {9},
pages = {948--960},
doi = {10.14778/3329772.3329773},
url = {https://doi.org/10.14778/3329772.3329773},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,081 | Co-movement Pattern Mining from Videos | 2024 | VLDB | 5.0830849e-05 |
| 11,247 | Mining Platoon Patterns from Traffic Videos | 2025 | VLDB | 4.9793485e-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 |
|---|---|---|---|---|
| 1,942 | Discovery of Convoys in Trajectory Databases | 2008 | VLDB | 9.3285345e-05 |
| 3,696 | A General and Parallel Platform for Mining Co-Movement Patterns over Large-scale Trajectories | 2017 | VLDB | 7.0918941e-05 |
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