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 |
|---|---|---|---|---|
| 1,337 | HoloDetect: Few-Shot Learning for Error Detection | 2019 | SIGMOD | 0.00012497164 |
| 1,612 | Detecting Data Errors: Where are we and what needs to be done? | 2016 | VLDB | 0.00011142794 |
| 1,894 | Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning | 2020 | VLDB | 0.0001018378 |
| 2,158 | Uni-Detect: A Unified Approach to Automated Error Detection in Tables | 2019 | SIGMOD | 9.4141354e-05 |
| 2,968 | Raha: A Configuration-Free Error Detection System | 2019 | SIGMOD | 7.7985097e-05 |
| 3,713 | GDR: A System for Guided Data Repair | 2010 | SIGMOD | 6.8224341e-05 |
| 5,729 | KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing | 2015 | VLDB | 5.3506368e-05 |
| 6,187 | Semi-Supervised Data Cleaning with Raha and Baran | 2021 | CIDR | 5.1656857e-05 |
| 6,350 | NADEEF: A Generalized Data Cleaning System | 2013 | VLDB | 5.101815e-05 |
| 6,993 | Unit Testing Data with Deequ | 2019 | SIGMOD | 4.8693227e-05 |
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