Data Incident Detection
Data Incident Detection is a methodological framework within data observability that identifies deviations from expected data behavior by systematically monitoring data pipelines for anomalies, inconsistencies, or quality violations in real time.
6
Publications
251
Citations
30
Authors
15
Institutions
Publications per year
2017–2026
1
Parents
6
Understanding Real World Data Corruptions in Cloud Systems
Peipei Wang, Daniel J. Dean, Xiaohui Gu · 2015 · 13 citations
1–5 of 6
30
University of Illinois Urbana-Champaign, United States
1 publications · 33 citations
1
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1
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1
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1
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1
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1–5 of 30
15
University of Illinois Urbana-Champaign
Urbana, United States
1 h-index
3 publications
99 citations

University of Electronic Science and Technology of China
Chengdu, China
1 h-index
4 publications
656 citations
Atac (United States)
Santa Clara, United States
1 h-index
5 publications
55 citations
University of Technology Sydney
Sydney, Australia
1 h-index
2 publications
32 citations
Consejo Superior de Investigaciones Científicas
Madrid, Spain
1 h-index
1 publications
14 citations

Ames Research Center
Mountain View, United States
1 h-index
1 publications
11 citations
Tianjin University
Tianjin, China
1 h-index
1 publications
16 citations
Artificial Intelligence Research Institute
Cerdanyola del Vallès, Spain
1 h-index
1 publications
14 citations
1–8 of 15
1 publications · 164 citations
1
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Conference · 1 publications · 11 citations
1
h-index
1 publications · 16 citations
1
h-index
1 publications · 14 citations
1
h-index
1–4