Boosting Moving Object Indexing through Velocity Partitioning
Summary: Introduces velocity partitioning: PCA/k-means finds dominant velocity axes and assigns objects to direction-specific TPR*/Bx-trees. Aligning indexes with skewed motion reduces search-space growth from quadratic to near-linear in maximum speed. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Thi Nguyen (La Trobe University)
- 2. Zhen He (La Trobe University)
- 3. Rui Zhang (University of Melbourne)
- 4. Phillip Ward (Commonwealth Scientific and Industrial Research Organisation)
BibTeX Citation
@article{nguyen_vldb12,
title = {{Boosting Moving Object Indexing through Velocity Partitioning}},
author = {Nguyen, Thi and He, Zhen and Zhang, Rui and Ward, Phillip},
journal = {PVLDB},
series = {{VLDB} '12},
volume = {5},
number = {9},
pages = {860--871},
doi = {10.14778/2311906.2311913},
url = {https://doi.org/10.14778/2311906.2311913},
year = {2012}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,070 | Indexing Methods for Moving Object Databases: Games and Other Applications | 2013 | SIGMOD | 5.7116663e-05 |
| 11,193 | BT-Tree: A Reinforcement Learning Based Index for Big Trajectory Data | 2024 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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