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Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search
Summary: Auto-Pipeline synthesizes multi-step data-cleaning pipelines from input tables and a target schema, rather than examples. It exploits implicit constraints (FDs, keys) to guide reinforcement learning and search, recovering ~70% of real pipelines up to 10 steps.
(summarized by gpt-5.6-luna on Jul 24 2026)
Paper ID
ha59dbf405ea88f97
Venue
VLDB
Year
2021
Pagerank
6.713221e-05
Overall Rank
4,235 | 71.53%
DOI
10.14778/3476249.3476303
Incoming Non-self Citations Over Time
BibTeX Citation
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@article{yang_vldb21,
title = {{Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search}},
author = {Yang, Junwen and He, Yeye and Chaudhuri, Surajit},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2563--2575},
doi = {10.14778/3476249.3476303},
url = {https://doi.org/10.14778/3476249.3476303},
year = {2021}
}
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Showing 10 of 10 citing papers.
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Venue
Pagerank
5,097
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HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation
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Auto-Prep: Holistic Prediction of Data Preparation Steps for Self-Service Business Intelligence
2025
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5.5020723e-05
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Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations
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4.9793485e-05
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LucidScript: Bottom-up Standardization for Data Preparation
2024
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4.9793485e-05
Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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