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BrewER: Entity Resolution On-Demand

Summary: BrewER enables on‑demand, prioritized entity resolution by progressively evaluating SQL selection/projection queries over dirty data and returning results as if issued on cleaned data according to a user-defined priority. Avoids full dataset cleaning for fast, resource-efficient, query-aware ER suited to interactive or frequently-changing data. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13256
Venue
VLDB
Year
2023
Pagerank
4.3366491e-05
Overall Rank
9,461 | 34.19%
DOI
10.14778/3611540.3611612

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
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
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

Rank Cited Paper Year Venue Pagerank
221 Deep Entity Matching with Pre-Trained Language Models 2021 VLDB 0.00033121824
300 Deep Learning for Entity Matching: A Design Space Exploration 2018 SIGMOD 0.00028441466
398 Big Data Integration 2013 VLDB 0.00024372588
712 Magellan: Toward Building Entity Matching Management Systems 2016 VLDB 0.00017732426
5,586 QuERy: A Framework for Integrating Entity Resolution with Query Processing 2016 VLDB 5.4219548e-05
6,175 Query-Driven Approach to Entity Resolution 2013 VLDB 5.169496e-05
8,008 Entity Resolution On-Demand 2022 VLDB 4.6067684e-05
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