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Multiscale Histograms: Summarizing Topological Relations in Large Spatial Datasets

Summary: Multiscale histograms of topological relations (contains, contained, overlap, disjoint) using Euler histograms for exact, constant-time aligned-window summaries. A 19/12-approx NP-hard storage-minimization, a k-histogram scheme for high accuracy, and constant-time approximate queries; experiments show large accuracy gains with scalable storage. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9217
Venue
VLDB
Year
2003
Pagerank
5.2143223e-05
Overall Rank
9,825 | 32.60%
DOI
10.1016/B978-012722442-8/50077-X

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{lin_vldb03,
        title = {{Multiscale Histograms: Summarizing Topological Relations in Large Spatial Datasets}},
        author = {Lin, Xuemin and Liu, Qing and Yuan, Yidong and Zhou, Xiaofang},
        journal = {PVLDB},
        series = {{VLDB} '03},
        doi = {10.1016/B978-012722442-8/50077-X},
        url = {https://doi.org/10.1016/B978-012722442-8/50077-X},
        year = {2003}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
1,170 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011827259
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