New Trends in Data Forgetting for Sustainable Data Management
Summary: Surveys data reduction advances—esp. submodular summarization—and argues their limits via the “data rotting” concept. Advocates explicit data-forgetting routines as a new stack, surveying existing methods and open algorithmic and systems challenges. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ramon Rico
- 2. Arno Siebes
- 3. Yannis Velegrakis
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 192 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB | 0.00035728858 |
| 754 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.00017117211 |
| 3,495 | Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources | 2015 | VLDB | 7.0400666e-05 |
| 4,375 | Sample Debiasing in the Themis Open World Database System | 2020 | SIGMOD | 6.2427076e-05 |
| 6,178 | A Database System with Amnesia | 2017 | CIDR | 5.1692384e-05 |
| 6,649 | Big Data Space Fungus | 2015 | CIDR | 4.9768878e-05 |
| 7,452 | PHOcus: Efficiently Archiving Photos | 2022 | VLDB | 4.7263711e-05 |
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