Data Quality Error Detection
Data Quality Error Detection is a systematic process within data quality management that identifies deviations from accuracy, completeness, consistency, and reliability standards in datasets through algorithmic, statistical, or rule-based methods to enable corrective actions.
32
Publications
1.5K
Citations
110
Authors
58
Institutions
Publications per year
2017–2026
12
Parents
32
Michele Dallachiesa, Amr Ebaid, Ahmed Eldawy et al. · 2013 · 306 citations
Ziawasch Abedjan, Xu Chu, Dong Deng et al. · Proceedings of the VLDB Endowment · 2016 · 237 citations
Combining quantitative and logical data cleaning
Nataliya Prokoshyna, Jaroslaw Szlichta, Fei Chiang et al. · Proceedings of the VLDB Endowment · 2015 · 104 citations
Shawn Turner, Luke Albert, Byron Gajewski et al. · Transportation Research Record Journal of the Transportation Research Board · 2000 · 76 citations
1–5 of 32
110
5
h-index
4
h-index
3
h-index
2
h-index
2
h-index
1–5 of 110
58

University of Waterloo
Waterloo, Canada
3 h-index
6 publications
660 citations

University of Arkansas at Little Rock
Little Rock, United States
2 h-index
6 publications
195 citations
AT&T (United States)
Bedminster, United States
2 h-index
3 publications
230 citations

Qatar University
Doha, Qatar
1 h-index
1 publications
46 citations

University of Edinburgh
Edinburgh, United Kingdom
1 h-index
1 publications
40 citations
University of Copenhagen
Copenhagen, Denmark
1 h-index
2 publications
62 citations

Technische Universität Darmstadt
Darmstadt, Germany
1 h-index
1 publications
12 citations
Center for Assessment
Dover, United States
1 h-index
1 publications
24 citations
1–8 of 58
PVLDB · 5 publications · 467 citations
5
h-index
2 publications · 38 citations
2
h-index
Repository · 2 publications · 79 citations
2
h-index
AJEE · 1 publications · 11 citations
1
h-index
1 publications · 33 citations
1
h-index
1–5