DBScholar

Back to papers

Semi-Supervised Data Cleaning with Raha and Baran

Summary: Raha and Baran are configuration-free semi-supervised systems for end-to-end error detection and correction that learn to combine an auto-generated pool of base detectors/correctors from ~20 labeled tuples via label propagation. They leverage transfer learning from prior cleaning tasks to speed up detection and improve correction effectiveness. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
412
Venue
CIDR
Year
2021
Pagerank
6.0720263e-05
Overall Rank
5,835 | 59.97%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{mahdavi_cidr21,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '21},
        title = {{Semi-Supervised Data Cleaning with Raha and Baran}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Mahdavi, Mohammad and Abedjan, Ziawasch},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers