Demonstrating Matelda for Multi-Table Error Detection
Summary: Matelda enables multi-table error detection by combining automated detectors with guided human-in-the-loop inspection to exploit cross-table relationships and shared context. It folds heterogeneous tables by domain/quality and uses semi-supervised label propagation to propagate labels and cut annotation cost. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,340 | HoloDetect: Few-Shot Learning for Error Detection | 2019 | SIGMOD | 0.00012492795 |
| 1,403 | Detecting Data Errors: Where are we and what needs to be done? | 2016 | VLDB | 0.00012180046 |
| 1,895 | Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning | 2020 | VLDB | 0.00010174634 |
| 2,161 | Uni-Detect: A Unified Approach to Automated Error Detection in Tables | 2019 | SIGMOD | 9.4029915e-05 |
| 2,968 | Raha: A Configuration-Free Error Detection System | 2019 | SIGMOD | 7.7964476e-05 |
| 3,690 | GDR: A System for Guided Data Repair | 2010 | SIGMOD | 6.8351349e-05 |
| 5,738 | KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing | 2015 | VLDB | 5.3454984e-05 |
| 6,188 | Semi-Supervised Data Cleaning with Raha and Baran | 2021 | CIDR | 5.1607275e-05 |
| 6,348 | NADEEF: A Generalized Data Cleaning System | 2013 | VLDB | 5.0969173e-05 |
| 6,991 | Unit Testing Data with Deequ | 2019 | SIGMOD | 4.8646637e-05 |
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