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Data-Independent Space Partitionings for Summaries

Summary: Poses continuous binning: select a small set of data-independent multidimensional bins (possibly overlapping) so query boxes can be approximated by additive composition, avoiding a single equiwidth grid. Defines quality metrics, shows NP-hardness, and gives algorithms (including synthetic point-set construction) for compact multi-histogram summaries that improve range-query accuracy while supporting dynamic updates and privacy-preserving publishing. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1843
Venue
PODS
Year
2021
Pagerank
5.093636e-05
Overall Rank
11,632 | 20.20%
DOI
10.1145/3452021.3458316

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BibTeX Citation

@inproceedings{cormode_pods21,
        address = {New York, NY, USA},
        series = {{PODS} '21},
        title = {{Data-Independent Space Partitionings for Summaries}},
        url = {https://dl.acm.org/doi/10.1145/3452021.3458316},
        doi = {10.1145/3452021.3458316},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Cormode, Graham and Garofalakis, Minos and Shekelyan, Michael},
        year = {2021}
}

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