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: 12396
Venue: VLDB
Year: 2021
Pagerank: 8.0301674e-05
Overall Rank: 2,845 | 80.23%
DOI: 10.14778/3476249.3476270

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 11 of 11 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,655	Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation	2022	VLDB	6.8723042e-05
4,126	Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization	2021	VLDB	6.4242e-05
5,023	AutoCTS: Automated Correlated Time Series Forecasting	2022	VLDB	5.7473072e-05
5,306	A Scalable AutoML Approach Based on Graph Neural Networks	2022	VLDB	5.5725759e-05
6,376	A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning	2023	SIGMOD	5.0861082e-05
6,489	Towards General and Efficient Online Tuning for Spark	2023	VLDB	5.0373773e-05
6,589	AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting	2023	SIGMOD	4.9964862e-05
7,493	SubStrat: A Subset-Based Optimization Strategy for Faster AutoML	2023	VLDB	4.7135369e-05
9,196	Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale	2022	VLDB	4.3723457e-05
9,787	The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format	2024	SIGMOD	4.2799988e-05
10,569	A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty	2025	VLDB	4.1905499e-05

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
192	HoloClean: Holistic Data Repairs with Probabilistic Inference	2017	VLDB	0.00035692958
252	Snorkel: Rapid Training Data Creation with Weak Supervision	2018	VLDB	0.00030532082
684	Cerebro: A Data System for Optimized Deep Learning Model Selection	2020	VLDB	0.00018152321
788	ActiveClean: Interactive Data Cleaning For Statistical Modeling	2016	VLDB	0.00016618698
1,354	Northstar: An Interactive Data Science System	2018	VLDB	0.00012424105
1,393	Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads	2018	VLDB	0.00012223372
2,122	SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle	2020	CIDR	9.4905306e-05
2,164	Elastic Machine Learning Algorithms in Amazon SageMaker	2020	SIGMOD	9.3953268e-05
2,759	Complaint-driven Training Data Debugging for Query 2.0	2020	SIGMOD	8.1646193e-05
2,871	Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations	2019	SIGMOD	7.9800983e-05
3,057	ZeroER: Entity Resolution using Zero Labeled Examples	2020	SIGMOD	7.6458287e-05

Semantically Similar Papers

Overall Rank	Paper	Year	Venue	Pagerank
557	SystemML: Declarative Machine Learning on Spark	2016	VLDB	0.00020186115
917	Democratizing Data Science through Interactive Curation of ML Pipelines	2019	SIGMOD	0.00015324193
5,244	Towards Benchmarking Feature Type Inference for AutoML Platforms	2021	SIGMOD	5.6021738e-05
5,965	Automatic Data Acquisition for Deep Learning	2021	VLDB	5.2476363e-05
4,592	Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift	2023	SIGMOD	6.056004e-05
2,164	Elastic Machine Learning Algorithms in Amazon SageMaker	2020	SIGMOD	9.3953268e-05
4,962	Doing More with Less: Characterizing Dataset Downsampling for AutoML	2021	VLDB	5.7979872e-05
7,309	The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development	2020	SIGMOD	4.7611148e-05
2,386	Oracle AutoML: A Fast and Predictive AutoML Pipeline	2020	VLDB	8.9167446e-05
5,306	A Scalable AutoML Approach Based on Graph Neural Networks	2022	VLDB	5.5725759e-05