Expand your Training Limits! Generating Training Data for ML-based Data Management

Summary: DataFarm generates and labels large, heterogeneous query workloads for ML-driven data management. A data-driven whitebox learner uses small workloads and data to synthesize jobs, delivering up to 9x labeling gains (R^2) and 54x cost reductions vs prior work. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h61725a5ba4ac531d
Venue: SIGMOD
Year: 2021
Pagerank: 5.8733296e-05
Overall Rank: 6,141 | 58.72%
DOI: 10.1145/3448016.3457286

Incoming Non-self Citations Over Time

Authors

1. Francesco Ventura (Politecnico di Torino)
2. Zoi Kaoudi (German National Research Center for Information Technology; Technical University of Berlin)
3. Jorge-Arnulfo Quiané-Ruiz (German National Research Center for Information Technology; Technical University of Berlin)
4. Volker Markl (German National Research Center for Information Technology; Technical University of Berlin)

BibTeX Citation

@inproceedings{ventura_sigmod21,
        title = {{Expand your Training Limits! Generating Training Data for ML-based Data Management}},
        author = {Ventura, Francesco and Kaoudi, Zoi and Quiané-Ruiz, Jorge-Arnulfo and Markl, Volker},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457286},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457286},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,885	Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction	2022	VLDB	7.9094988e-05
7,033	Rethinking Learned Cost Models: Why Start from Scratch?	2023	SIGMOD	5.6168499e-05
7,460	T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees	2025	SIGMOD	5.5215755e-05
9,067	TensorSocket: Shared Data Loading for Deep Learning Training	2026	SIGMOD	5.2283159e-05
9,133	Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems	2024	VLDB	5.2229655e-05
9,533	Farm Your ML-based Query Optimizer's Food! - Human-Guided Training Data Generation -	2022	CIDR	5.1636858e-05
9,706	Database Gyms	2023	CIDR	5.1376763e-05
9,781	Optimizing Dataflow Systems for Scalable Interactive Visualization	2024	SIGMOD	5.1300393e-05
11,425	Learned Cost Models for Query Optimization: From Batch to Streaming Systems	2025	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 22 of 22 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061066921
85	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035864347
98	LEO - DB2's LEarning Optimizer	2001	VLDB	0.00034106982
145	Neo: A Learned Query Optimizer	2019	VLDB	0.0002908188
205	Snorkel: Rapid Training Data Creation with Weak Supervision	2018	VLDB	0.00025181304
318	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021167555
406	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00019045544
461	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.00017829982
479	The Making of TPC-DS	2006	VLDB	0.00017622471
534	On Active Learning of Record Matching Packages	2010	SIGMOD	0.0001680637
560	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.00016403151
1,279	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00011226878
1,580	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	0.00010180835
2,275	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	8.7090584e-05
2,842	Query Performance Prediction for Concurrent Queries using Graph Embedding	2020	VLDB	7.949193e-05
3,271	Estimating Cardinalities with Deep Sketches	2019	SIGMOD	7.4744941e-05
3,402	RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -	2018	VLDB	7.3304477e-05
5,386	PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics	2013	VLDB	6.1522468e-05
5,656	Uncertainty Aware Query Execution Time Prediction	2014	VLDB	6.0488629e-05
5,981	Active Learning of GAV Schema Mappings	2018	PODS	5.9278637e-05
8,288	Learning Table Access Cardinalities with LEO	2002	SIGMOD	5.3614914e-05
10,161	Rheem: Enabling Multi-Platform Task Execution	2016	SIGMOD	5.0688288e-05

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