DAFDiscover: Robust Mining Algorithm for Dynamic Approximate Functional Dependencies on Dirty Data
Summary: Introduces Dynamic Approximate Functional Dependencies (DAFDs), adapting relaxation to attribute error rates for semantically stronger dependency discovery on dirty data. DAFDiscover mines DAFDs with SOTA AFD complexity, backed by FD-equivalence, inference, probability, and validity-bound theory. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaoou Ding (Harbin Engineering University)
- 2. Yixing Lu (Harbin Engineering University)
- 3. Hongzhi Wang (Harbin Engineering University)
- 4. Chen Wang (Tsinghua University)
- 5. Yida Liu (Harbin Engineering University)
- 6. Jianmin Wang (Tsinghua University)
BibTeX Citation
@article{ding_vldb24,
title = {{DAFDiscover: Robust Mining Algorithm for Dynamic Approximate Functional Dependencies on Dirty Data}},
author = {Ding, Xiaoou and Lu, Yixing and Wang, Hongzhi and Wang, Chen and Liu, Yida and Wang, Jianmin},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3484--3496},
doi = {10.14778/3681954.3682015},
url = {https://doi.org/10.14778/3681954.3682015},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,794 | Storage-Centric Relation Design via High-Quality Approximate Functional Dependencies | 2026 | VLDB | 4.9793485e-05 |
| 11,254 | Efficient Discovery of Relaxed Functional Dependencies | 2025 | VLDB | 4.9793485e-05 |
| 11,353 | UniClean: A Scalable Data Cleaning Solution for Mixed Errors based on Unified Cleaners and Optimized Cleaning Workflow | 2025 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 160 | CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies | 2004 | SIGMOD | 0.00027837289 |
| 603 | A Hybrid Approach to Functional Dependency Discovery | 2016 | SIGMOD | 0.00015699254 |
| 649 | Discovering Data Quality Rules | 2008 | VLDB | 0.00015149865 |
| 1,488 | Efficient Discovery of Approximate Dependencies | 2018 | VLDB | 0.00010517437 |
| 2,051 | Messing Up with BART: Error Generation for Evaluating Data-Cleaning Algorithms | 2016 | VLDB | 9.1199511e-05 |
| 3,193 | A Statistical Perspective on Discovering Functional Dependencies in Noisy Data | 2020 | SIGMOD | 7.5505481e-05 |
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| 1 | 2,125 | Discovery of Genuine Functional Dependencies from Relational Data with Missing Values | 2018 | VLDB |
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| 5 | 727 | Functional Dependency Discovery: An Experimental Evaluation of Seven Algorithms | 2015 | VLDB |
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| 8 | 603 | A Hybrid Approach to Functional Dependency Discovery | 2016 | SIGMOD |
| 9 | 10,106 | Efficient Differential Dependency Discovery | 2024 | VLDB |
| 10 | 11,254 | Efficient Discovery of Relaxed Functional Dependencies | 2025 | VLDB |