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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)

Paper ID
10714
Venue
VLDB
Year
2012
Pagerank
5.297624e-05
Overall Rank
9,254 | 36.51%
DOI
10.14778/2311906.2311913

Incoming Non-self Citations Over Time

Authors

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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