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PPQ-Trajectory: Spatio-temporal Quantization for Querying in Large Trajectory Repositories

Summary: PPQ-trajectory introduces spatio-temporal quantization for querying large dynamic trajectories. Partition-wise predictive quantizer yields an error-bounded codebook with autocorrelation and spatial partitions, enabling approximate and exact queries; quadtrees and incremental temporal indexing allow fast, reconstruction-free querying on compressed trajectories, with experiments showing improved accuracy and compression. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12581
Venue
VLDB
Year
2021
Pagerank
5.8700652e-05
Overall Rank
6,472 | 55.60%
DOI
10.14778/3425879.3425891

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BibTeX Citation

@article{wang_vldb21,
        title = {{PPQ-Trajectory: Spatio-temporal Quantization for Querying in Large Trajectory Repositories}},
        author = {Wang, Shuang and Ferhatosmanoglu, Hakan},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {2},
        pages = {215--227},
        doi = {10.14778/3425879.3425891},
        url = {https://doi.org/10.14778/3425879.3425891},
        year = {2021}
}

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