Pipeline Automation
Pipeline Automation is a methodological framework within data engineering that systematically orchestrates, executes, and monitors data pipeline workflows with minimal human intervention by leveraging predefined rules, scheduling mechanisms, and dependency management to ensure consistent, scalable, and reproducible data processing.
39
Publications
2.5K
Citations
151
Authors
70
Institutions
Publications per year
2017–2026
15
Parents
39
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz et al. · 2016 · 571 citations · Full text
1–5 of 39
151
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University of Illinois Urbana-Champaign, United States
2 publications · 82 citations
2
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2
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1–5 of 151
70
University of Chicago
Chicago, United States
4 h-index
6 publications
679 citations
University of Pennsylvania
Philadelphia, United States
3 h-index
10 publications
1.9K citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
596 citations
California University of Pennsylvania
California, United States
2 h-index
5 publications
164 citations
Harvard University
Cambridge, United States
2 h-index
9 publications
132 citations
The University of Tokyo
Tokyo, Japan
2 h-index
3 publications
57 citations

Daresbury Laboratory
Daresbury, United Kingdom
2 h-index
4 publications
690 citations

China University of Petroleum, Beijing
Beijing, China
2 h-index
7 publications
142 citations
1–8 of 70
Repository · 2 publications · 60 citations
2
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2 publications · 75 citations
2
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2 publications · 82 citations
2
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2 publications · 345 citations
2
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1 publications · 21 citations
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