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Efficient framework for operating on data sketches

Summary: Framework to estimate results of arbitrary sequences of set-theory operations on concise data sketches, enabling compositional analysis over massive streams. New sketching algorithm cuts average comparisons from O(n) to O(log n) and proves the prior estimator is the MLE. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13052
Venue
VLDB
Year
2023
Pagerank
4.5086031e-05
Overall Rank
8,451 | 41.21%
DOI
10.14778/3594512.3594526

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,038 OmniSketch: Efficient Multi-Dimensional High-Velocity Stream Analytics with Arbitrary Predicates 2024 VLDB 4.4039656e-05
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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
402 Mergeable Summaries 2012 PODS 0.00024196343
5,200 SetSketch: Filling the Gap between MinHash and HyperLogLog 2021 VLDB 5.6337581e-05
8,452 On the algebra of data sketches 2021 VLDB 4.5086031e-05
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