Boosting Moving Object Indexing through Velocity Partitioning
Summary: VP exploits skewed velocity distributions by identifying dominant velocity axes (DVAs) using PCA and k-means. Per-DVA indexes (e.g., TPR*-tree, Bx-tree) keep objects on near-1D directions, reducing search-space growth from quadratic to near-linear with speed. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Thi Nguyen
- 2. Zhen He
- 3. Rui Zhang
- 4. Phillip Ward
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,202 | Indexing Methods for Moving Object Databases: Games and Other Applications | 2013 | SIGMOD | 4.7975642e-05 |
| 10,983 | BT-Tree: A Reinforcement Learning Based Index for Big Trajectory Data | 2024 | SIGMOD | 4.1905499e-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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| 2,558 | Novel Approaches to the Indexing of Moving Object Trajectories | 2000 | VLDB | 8.5441554e-05 |
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