RadlER: Deduplicated Sampling On-Demand
Summary: RadlER enables deduplicated sampling on-demand by incrementally cleaning only the entities required to meet a target distribution over subpopulations, avoiding costly full-dataset deduplication. Interactive demo shows a workflow that saves substantial time and resources for downstream analyses. (summarized by gpt-5-mini on Feb 09 2026)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,018 | Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching | 2023 | VLDB | 6.5244015e-05 |
| 8,008 | Entity Resolution On-Demand | 2022 | VLDB | 4.6067684e-05 |
| 9,461 | BrewER: Entity Resolution On-Demand | 2023 | VLDB | 4.3366491e-05 |
| 10,617 | Deduplicated Sampling On-Demand | 2025 | VLDB | 4.1945683e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,435 | Sampling Dirty Data for Matching Attributes | 2010 | SIGMOD | 6.1918164e-05 |
| 4,619 | Crowd-Based Deduplication: An Adaptive Approach | 2015 | SIGMOD | 6.0444854e-05 |
| 8,406 | DADER: Hands-Off Entity Resolution with Domain Adaptation | 2022 | VLDB | 4.5220083e-05 |
| 2,231 | Dedoop: Efficient Deduplication with Hadoop | 2012 | VLDB | 9.2304499e-05 |
| 8,910 | R2D2: Reducing Redundancy and Duplication in Data Lakes | 2023 | SIGMOD | 4.427232e-05 |
| 8,008 | Entity Resolution On-Demand | 2022 | VLDB | 4.6067684e-05 |
| 6,042 | MDedup: Duplicate Detection with Matching Dependencies | 2020 | VLDB | 5.2405269e-05 |
| 3,582 | NADEEF/ER: Generic and Interactive Entity Resolution | 2014 | SIGMOD | 6.9479263e-05 |
| 9,461 | BrewER: Entity Resolution On-Demand | 2023 | VLDB | 4.3366491e-05 |
| 10,617 | Deduplicated Sampling On-Demand | 2025 | VLDB | 4.1945683e-05 |