Raha: A Configuration-Free Error Detection System
Summary: Raha is a configuration-free error detection system for data cleaning. It generates a compact set of configurations to form per-tuple feature vectors, then uses sampling and learning to select representative values, leveraging historical data to prune irrelevant detectors and outperform prior work with at most 20 labels. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Mohammad Mahdavi (Technical University of Berlin)
- 2. Ziawasch Abedjan (Technical University of Berlin)
- 3. Raul Castro Fernandez (Massachusetts Institute of Technology)
- 4. Samuel Madden (Massachusetts Institute of Technology)
- 5. Mourad Ouzzani (Hamad Bin Khalifa University; Qatar Computing Research Institute)
- 6. Michael Stonebraker (Massachusetts Institute of Technology)
- 7. Nan Tang (Hamad Bin Khalifa University; Qatar Computing Research Institute)
BibTeX Citation
@inproceedings{mahdavi_sigmod19,
title = {{Raha: A Configuration-Free Error Detection System}},
author = {Mahdavi, Mohammad and Abedjan, Ziawasch and Fernandez, Raul Castro and Madden, Samuel and Ouzzani, Mourad and Stonebraker, Michael and Tang, Nan},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3324956},
url = {https://dl.acm.org/doi/10.1145/3299869.3324956},
year = {2019}
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