Data Pipeline Optimization is a methodological framework within data orchestration that systematically enhances the efficiency, reliability, and scalability of data workflows by minimizing latency, reducing resource consumption, and ensuring timely data delivery across distributed systems.
26
Publications
1.4K
Citations
88
Authors
41
Institutions
Publications per year
2017–2026
19
Parents
Publications
26
Data Management Challenges in Production Machine Learning
Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang et al. · 2017 · 209 citations · Full text
Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
Tang Jun, Aleksandra Korolova, Xiaolong Bai et al. · arXiv (Cornell University) · 2017 · 186 citations · Full text
1–5 of 26
Authors
88
- GN
2
h-index
- AB
2
h-index
- AK
1
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- CZ
1
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- CA
1
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1–5 of 88
Institutions
41
The Ohio State University
Columbus, United States
2 h-index
4 publications
97 citations
University of Southern California
Los Angeles, United States
2 h-index
3 publications
69 citations
Google (United States)
Mountain View, United States
2 h-index
5 publications
893 citations
University of Toronto
Toronto, Canada
1 h-index
5 publications
245 citations
UNSW Sydney
Sydney, Australia
1 h-index
6 publications
456 citations
University of Illinois Urbana-Champaign
Urbana, United States
1 h-index
1 publications
20 citations
St George Hospital
Sydney, Australia
1 h-index
1 publications
76 citations
Garvan Institute of Medical Research
Darlinghurst, Australia
1 h-index
13 publications
988 citations
1–8 of 41
Venues
- PLoS ONE
2 publications · 31 citations
2
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- Proceedings of the ACM on Management of Data
1 publications · 13 citations
2
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- Big Data
1 publications · 12 citations
1
h-index
- BMC Genomics
1 publications · 265 citations
1
h-index
- IEEE/ACM Transactions on Networking
TON · 1 publications · 77 citations
1
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1–5