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SWS: A Complexity-Optimized Solution for Spatial-Temporal Kernel Density Visualization

Summary: SWS introduces a sliding-window based STKDV method with reduced time complexity and unchanged space usage. Paired with a progressive visualization framework, it yields coarse-to-fine partial results and achieves 1.71x–24x speedups on large-scale data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
13140
Venue
VLDB
Year
2022
Pagerank
5.093636e-05
Overall Rank
11,619 | 20.29%
DOI
10.14778/3503585.3503591

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BibTeX Citation

@article{chan_vldb22,
        title = {{SWS: A Complexity-Optimized Solution for Spatial-Temporal Kernel Density Visualization}},
        author = {Chan, Tsz Nam and Ip, Pak Lon and U, Leong Hou and Choi, Byron and Xu, Jianliang},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {4},
        pages = {814--827},
        doi = {10.14778/3503585.3503591},
        url = {https://doi.org/10.14778/3503585.3503591},
        year = {2022}
}

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