HAIPipe: Combining Human-generated and Machine-generated Pip…
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- DOI 10.1145/3588945
- OpenAlex ID W4380433113
- Type Article
- Language EN
- Published 26 May 2023
- Volume 1 (1)
- Pages 1–26
- Source Proceedings of the ACM on Management of …
- Publisher Association for Computing Machinery
- FWCI 2.27
- Countries 1
- Institutions 3
- Added 10 Oct 2025
- Updated 16 Oct 2026
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HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation
Article 2023 EN
Data preparation is crucial in achieving optimized results for machine learning (ML). However, having a good data preparation pipeline is highly non-trivial for ML practitioners, which is not only domain-specific, but also dataset-specific. There are two common practices. Human-generated pipelines (HI-pipelines) typically use a wide range of any operations or libraries but are highly experience- a…
26
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21
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7
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2.27
FWCI
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Authors & Affiliations
7 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Sibei Chen | Renmin University of China (CN) | |
| 2 | Nan Tang | — | |
| 3 | Ju Fan | Renmin University of China (CN) | |
| 4 | Xuemi Yan | Renmin University of China (CN) | |
| 5 | Chengliang Chai | Beijing Institute of Technolog… (CN) | |
| 6 | Guoliang Li | Tsinghua University (CN) | |
| 7 | Xiaoyong Du | Renmin University of China (CN) |
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