Data Lake Data Quality is a scholarly construct that defines the extent to which data within a data lake meets specified criteria of accuracy, completeness, consistency, timeliness, and reliability, distinguishing it from general data quality by addressing the unique challenges of unstructured, schema-on-read, and highly scalable data environments.
8
Publications
325
Citations
27
Authors
20
Institutions
Publications per year
2017–2026
5
Parents
Publications
8
Mina H. Farid, Alexandra Roatiş, Ihab F. Ilyas et al. · 2016 · 76 citations
Ying Yang, Niccolò Meneghetti, Ronny Fehling et al. · Proceedings of the VLDB Endowment · 2015 · 37 citations
1–5 of 8
Authors
27
- ÓP
1
h-index
- IF
1
h-index
- RF
1
h-index
- JS
1
h-index
- YY
1
h-index
1–5 of 27
Institutions
20
University of Novi Sad
Novi Sad, Serbia
1 h-index
1 publications
16 citations
Institute of Tibetan Plateau Research
Beijing, China
1 h-index
1 publications
20 citations
Buffalo State University
Buffalo, United States
1 h-index
3 publications
111 citations
University of Michigan
Ann Arbor, United States
1 h-index
1 publications
23 citations
California Southern University
Chandler, United States
1 h-index
1 publications
101 citations
University of Tartu
Tartu, Estonia
1 h-index
1 publications
16 citations
Software (Spain)
Tres Cantos, Spain
1 h-index
2 publications
20 citations
Thomson Reuters (Canada)
Toronto, Canada
1 h-index
1 publications
76 citations
1–8 of 20
Venues
- International Journal of Applied Mathematics and Computer Science
1 publications · 42 citations
1
h-index
- Scientific Data
1 publications · 20 citations
1
h-index
- Proceedings of the VLDB Endowment
PVLDB · 1 publications · 37 citations
1
h-index
- Procedia Computer Science
1 publications · 16 citations
1
h-index
- IEEE Intelligent Systems
1 publications · 101 citations
1
h-index
1–5