HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation
Summary: HAIPipe fuses HI-pipelines with AI-pipelines to form HAI-pipelines that outperform either. It uses an enumeration-sampling framework and RL-guided AI-pipeline search, with experiments on 1400+ real-world HI-pipelines showing gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sibei Chen (Renmin University of China)
- 2. Nan Tang (Hong Kong University of Science and Technology; Qatar Computing Research Institute)
- 3. Ju Fan (Renmin University of China)
- 4. Xuemi Yan (Renmin University of China)
- 5. Chengliang Chai (Beijing Institute of Technology)
- 6. Guoliang Li (Tsinghua University)
- 7. Xiaoyong Du (Renmin University of China)
BibTeX Citation
@inproceedings{chen_sigmod23,
title = {{HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation}},
author = {Chen, Sibei and Tang, Nan and Fan, Ju and Yan, Xuemi and Chai, Chengliang and Li, Guoliang and Du, Xiaoyong},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588945},
url = {https://dl.acm.org/doi/10.1145/3588945},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,856 | Automatic Database Configuration Debugging using Retrieval-Augmented Language Models | 2025 | SIGMOD | 6.0644656e-05 |
| 6,254 | In-depth Analysis of Graph-based RAG in a Unified Framework | 2025 | VLDB | 5.9405174e-05 |
| 8,797 | Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations | 2024 | SIGMOD | 5.3702298e-05 |
| 8,914 | CtxPipe: Context-aware Data Preparation Pipeline Construction for Machine Learning | 2024 | SIGMOD | 5.3483178e-05 |
| 10,253 | HAMMER: An Automatic RAG Tuning System via Hierarchical Memory-Guided Monte Carlo Tree Search | 2026 | SIGMOD | 5.093636e-05 |
| 10,931 | AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,004 | Democratizing Data Science through Interactive Curation of ML Pipelines | 2019 | SIGMOD | 0.00012701932 |
| 2,029 | Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads | 2018 | VLDB | 9.2843642e-05 |
| 2,058 | Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning | 2020 | SIGMOD | 9.2480248e-05 |
| 2,744 | Auto-Suggest: Learning-to-Recommend Data Preparation Steps Using Data Science Notebooks | 2020 | SIGMOD | 8.1781662e-05 |
| 4,875 | Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search | 2021 | VLDB | 6.4689177e-05 |
| 5,730 | Domain Adaptation for Deep Entity Resolution | 2022 | SIGMOD | 6.1071585e-05 |
| 8,336 | DADER: Hands-Off Entity Resolution with Domain Adaptation | 2022 | VLDB | 5.451516e-05 |
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