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Entity Resolution On-Demand

Summary: BrewER enables on-demand entity resolution by answering SQL SP queries over dirty data while progressively returning results as if cleaned. It cleans entities one-by-one in ORDER BY order, enabling top-k, stop-and-resume, and substantial resource savings, with experiments on four real-world datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12656
Venue
VLDB
Year
2022
Pagerank
4.6067684e-05
Overall Rank
8,008 | 44.30%
DOI
10.14778/3523210.3523226

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank Citing Paper Year Venue Pagerank
5,840 Logical and Physical Optimizations for SQL Query Execution over Large Language Models 2025 SIGMOD 5.3042561e-05
9,461 BrewER: Entity Resolution On-Demand 2023 VLDB 4.3366491e-05
9,855 Progressive Entity Matching: A Design Space Exploration 2025 SIGMOD 4.269353e-05
10,617 Deduplicated Sampling On-Demand 2025 VLDB 4.1945683e-05
10,807 RadlER: Deduplicated Sampling On-Demand 2025 VLDB 4.1945683e-05
11,006 FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data 2024 VLDB 4.1945683e-05
11,373 Generalized Supervised Meta-blocking 2022 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

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