Demonstrating Matelda for Multi-Table Error Detection
Summary: Matelda tackles cross-table error detection where isolated-table cleaners miss relationships in heterogeneous, independently managed data. Its interactive Inspection & Action workflow combines folding, semi-supervised label propagation, contextual support, and collaboration to reduce annotation effort. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Fatemeh Ahmadi (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 2. Julian Paulußen (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 3. Ziawasch Abedjan (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
BibTeX Citation
@article{ahmadi_vldb25,
title = {{Demonstrating Matelda for Multi-Table Error Detection}},
author = {Ahmadi, Fatemeh and Paulußen, Julian and Abedjan, Ziawasch},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5379--5382},
doi = {10.14778/3750601.3750676},
url = {https://doi.org/10.14778/3750601.3750676},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 946 | HoloDetect: Few-Shot Learning for Error Detection | 2019 | SIGMOD | 0.00013054126 |
| 1,351 | Detecting Data Errors: Where are we and what needs to be done? | 2016 | VLDB | 0.00011064851 |
| 1,476 | Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning | 2020 | VLDB | 0.00010659277 |
| 2,297 | Raha: A Configuration-Free Error Detection System | 2019 | SIGMOD | 8.7897221e-05 |
| 2,728 | Uni-Detect: A Unified Approach to Automated Error Detection in Tables | 2019 | SIGMOD | 8.1995954e-05 |
| 4,059 | GDR: A System for Guided Data Repair | 2010 | SIGMOD | 6.9330647e-05 |
| 5,397 | KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing | 2015 | VLDB | 6.232136e-05 |
| 5,835 | Semi-Supervised Data Cleaning with Raha and Baran | 2021 | CIDR | 6.0720263e-05 |
| 6,050 | NADEEF: A Generalized Data Cleaning System | 2013 | VLDB | 5.9945626e-05 |
| 6,801 | Unit Testing Data with Deequ | 2019 | SIGMOD | 5.7690726e-05 |
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