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Distinct Sampling on Streaming Data with Near-Duplicates

Summary: Introduces distinct sampling for streams with near-duplicates, treating geometric clusters as one item and sampling representatives uniformly. Provides provable Euclidean-space algorithms, including sliding-window variants, with strong empirical validation. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
1757
Venue
PODS
Year
2018
Pagerank
5.093636e-05
Overall Rank
11,897 | 18.38%
DOI
10.1145/3196959.3196978

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Authors

BibTeX Citation

@inproceedings{chen_pods18,
        address = {New York, NY, USA},
        series = {{PODS} '18},
        title = {{Distinct Sampling on Streaming Data with Near-Duplicates}},
        url = {https://dl.acm.org/doi/10.1145/3196959.3196978},
        doi = {10.1145/3196959.3196978},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Chen, Jiecao and Zhang, Qin},
        year = {2018}
}

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