Efficient framework for operating on data sketches

Summary: A framework estimates arbitrary sequences of set operations directly from concise data sketches. Its sketching algorithm reduces comparisons from O(n) to average O(log n), and establishes the prior estimator as maximum likelihood. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 13240
Venue: VLDB
Year: 2023
Pagerank: 5.3884066e-05
Overall Rank: 8,666 | 40.55%
DOI: 10.14778/3594512.3594526

Incoming Non-self Citations Over Time

Authors

1. Jakub Lemiesz (Wroclaw University of Science and Technology)

BibTeX Citation

@article{lemiesz_vldb23,
        title = {{Efficient framework for operating on data sketches}},
        author = {Lemiesz, Jakub},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {8},
        pages = {1967--1978},
        doi = {10.14778/3594512.3594526},
        url = {https://doi.org/10.14778/3594512.3594526},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,188	OmniSketch: Efficient Multi-Dimensional High-Velocity Stream Analytics with Arbitrary Predicates	2024	VLDB	5.3058708e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
451	Mergeable Summaries	2012	PODS	0.00018151445
5,864	SetSketch: Filling the Gap between MinHash and HyperLogLog	2021	VLDB	6.0619574e-05
8,667	On the algebra of data sketches	2021	VLDB	5.3884066e-05

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