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Join-Distinct Aggregate Estimation over Update Streams

Summary: First space-efficient algorithms for Join-Distinct (distinct-projection over joins) on general update streams (inserts+deletes), introducing JD sketches — a new class of hash-based synopses that are built per stream and combinable. Probabilistic estimators yield low-error, high-confidence estimates with small per-update time and space, backed by near-optimal lower bounds and empirical validation. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1358
Venue
PODS
Year
2005
Pagerank
5.093636e-05
Overall Rank
12,724 | 12.71%
DOI
10.1145/1065167.1065200

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BibTeX Citation

@inproceedings{ganguly_pods05,
        address = {New York, NY, USA},
        series = {{PODS} '05},
        title = {{Join-Distinct Aggregate Estimation over Update Streams}},
        url = {https://dl.acm.org/doi/10.1145/1065167.1065200},
        doi = {10.1145/1065167.1065200},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Ganguly, Sumit and Garofalakis, Minos and Kumar, Amit and Rastogi, Rajeev},
        year = {2005}
}

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