Automated Data Quality Rules
Automated Data Quality Rules is a formal method in data management that systematically enforces predefined data quality constraints through algorithmic validation and correction processes to ensure consistency, accuracy, and completeness of data without manual intervention.
9
Publications
528
Citations
26
Authors
20
Institutions
Publications per year
2017–2026
4
Parents
9
Holistic data cleaning: Putting violations into context
Xu Chu, Ihab F. Ilyas, Paolo Papotti · 2013 · 319 citations
Deducing certain fixes to graphs
Wenfei Fan, Ping Lü, Chao Tian et al. · Proceedings of the VLDB Endowment · 2019 · 40 citations
RuleMiner: Data quality rules discovery
Xu Chu, Ihab F. Ilyas, Paolo Papotti et al. · 2014 · 29 citations
1–5 of 9
26
2
h-index
2
h-index
Indiana University – Purdue University Indianapolis, United States
1 publications · 15 citations
1
h-index
1
h-index
1
h-index
1–5 of 26
20

University of Waterloo
Waterloo, Canada
2 h-index
3 publications
377 citations

Children's Hospital of Philadelphia
Philadelphia, United States
1 h-index
1 publications
15 citations

Beihang University
Beijing, China
1 h-index
2 publications
80 citations
KU Leuven
Leuven, Belgium
1 h-index
3 publications
132 citations
Indiana University
Bloomington, United States
1 h-index
2 publications
30 citations

Concordia University
Montreal, Canada
1 h-index
1 publications
32 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
2 publications
30 citations
Indiana University Bloomington
Bloomington, United States
1 h-index
1 publications
15 citations
1–8 of 20
Book series · 1 publications · 15 citations
1
h-index
1 publications · 44 citations
1
h-index
1 publications · 12 citations
1
h-index
GRSL · 1 publications · 15 citations
1
h-index
PVLDB · 1 publications · 40 citations
1
h-index
1–5