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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Authors
- 1. Luca Zecchini (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 2. Ziawasch Abedjan (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 3. Vasilis Efthymiou (Harokopio University)
- 4. Giovanni Simonini (University of Modena and Reggio Emilia)
BibTeX Citation
@article{zecchini_vldb25,
title = {{RadlER: Deduplicated Sampling On-Demand}},
author = {Zecchini, Luca and Abedjan, Ziawasch and Efthymiou, Vasilis and Simonini, Giovanni},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5319--5322},
doi = {10.14778/3750601.3750661},
url = {https://doi.org/10.14778/3750601.3750661},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,489 | Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching | 2023 | VLDB | 6.200289e-05 |
| 7,743 | Entity Resolution On-Demand | 2022 | VLDB | 5.5545622e-05 |
| 9,381 | Deduplicated Sampling On-Demand | 2025 | VLDB | 5.2755515e-05 |
| 9,611 | BrewER: Entity Resolution On-Demand | 2023 | VLDB | 5.2460348e-05 |
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