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VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition

Summary: VolcanoML enables scalable AutoML by decomposing large search spaces (feature engineering, model selection, hyperparameters) into smaller subspaces via building blocks and a plan. It uses a Volcano-style, DB-inspired executor to run plans, delivering expressive decomposition and faster results than auto-sklearn. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12583
Venue
VLDB
Year
2021
Pagerank
7.5775321e-05
Overall Rank
3,272 | 77.56%
DOI
10.14778/3476249.3476270

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{li_vldb21,
        title = {{VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition}},
        author = {Li, Yang and Shen, Yu and Zhang, Wentao and Jiang, Jiawei and Ding, Bolin and Li, Yaliang and Zhou, Jingren and Yang, Zhi and Wu, Wentao and Zhang, Ce and Cui, Bin},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {11},
        pages = {2167--2176},
        doi = {10.14778/3476249.3476270},
        url = {https://doi.org/10.14778/3476249.3476270},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 11 of 11 citing papers.

Rank Citing Paper Year Venue Pagerank
2,740 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.1855759e-05
3,850 Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization 2021 VLDB 7.0742748e-05
5,129 AutoCTS: Automated Correlated Time Series Forecasting 2022 VLDB 6.3548172e-05
5,901 A Scalable AutoML Approach Based on Graph Neural Networks 2022 VLDB 6.0467725e-05
6,344 Towards General and Efficient Online Tuning for Spark 2023 VLDB 5.9060457e-05
6,443 AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting 2023 SIGMOD 5.8775356e-05
6,600 A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning 2023 SIGMOD 5.8250114e-05
7,127 SubStrat: A Subset-Based Optimization Strategy for Faster AutoML 2023 VLDB 5.6957765e-05
9,290 Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale 2022 VLDB 5.2910774e-05
9,881 The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format 2024 SIGMOD 5.2040783e-05
10,827 A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
112 HoloClean: Holistic Data Repairs with Probabilistic Inference 2017 VLDB 0.00032801121
205 Snorkel: Rapid Training Data Creation with Weak Supervision 2018 VLDB 0.00025235185
582 ActiveClean: Interactive Data Cleaning For Statistical Modeling 2016 VLDB 0.00016148948
1,157 Cerebro: A Data System for Optimized Deep Learning Model Selection 2020 VLDB 0.00011924049
1,392 Northstar: An Interactive Data Science System 2018 VLDB 0.00010936065
1,756 SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle 2020 CIDR 9.8172465e-05
1,937 Elastic Machine Learning Algorithms in Amazon SageMaker 2020 SIGMOD 9.4524758e-05
2,029 Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads 2018 VLDB 9.2843642e-05
2,290 ZeroER: Entity Resolution using Zero Labeled Examples 2020 SIGMOD 8.799251e-05
2,584 Complaint-driven Training Data Debugging for Query 2.0 2020 SIGMOD 8.3783546e-05
2,987 Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations 2019 SIGMOD 7.8907997e-05
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