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: 5.8662826e-05
Overall Rank: 6,113 | 59.03%
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
2,619	Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction	2022	VLDB	8.1985665e-05
6,158	Rethinking Learned Cost Models: Why Start from Scratch?	2023	SIGMOD	5.8518237e-05
7,483	T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees	2025	SIGMOD	5.5007125e-05
9,097	TensorSocket: Shared Data Loading for Deep Learning Training	2026	SIGMOD	5.208561e-05
9,154	Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems	2024	VLDB	5.2050937e-05
9,567	Farm Your ML-based Query Optimizer's Food! - Human-Guided Training Data Generation -	2022	CIDR	5.1442061e-05
9,731	Database Gyms	2023	CIDR	5.120066e-05
9,819	Optimizing Dataflow Systems for Scalable Interactive Visualization	2024	SIGMOD	5.1106557e-05
11,484	Learned Cost Models for Query Optimization: From Batch to Streaming Systems	2025	VLDB	4.9605343e-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	0.00061463175
84	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035920657
98	LEO - DB2's LEarning Optimizer	2001	VLDB	0.00034050614
142	Neo: A Learned Query Optimizer	2019	VLDB	0.00029114937
200	Snorkel: Rapid Training Data Creation with Weak Supervision	2018	VLDB	0.00025443474
320	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021128745
405	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00019020138
453	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.0001801482
473	The Making of TPC-DS	2006	VLDB	0.00017656775
535	On Active Learning of Record Matching Packages	2010	SIGMOD	0.00016768308
545	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.0001657599
1,282	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00011197535
1,583	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	0.00010148362
2,290	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	8.6813363e-05
2,768	Query Performance Prediction for Concurrent Queries using Graph Embedding	2020	VLDB	8.0199086e-05
3,282	Estimating Cardinalities with Deep Sketches	2019	SIGMOD	7.4491295e-05
3,416	RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -	2018	VLDB	7.3046808e-05
5,394	PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics	2013	VLDB	6.1351004e-05
5,670	Uncertainty Aware Query Execution Time Prediction	2014	VLDB	6.0266818e-05
5,992	Active Learning of GAV Schema Mappings	2018	PODS	5.9058443e-05
8,309	Learning Table Access Cardinalities with LEO	2002	SIGMOD	5.3415206e-05
10,200	Rheem: Enabling Multi-Platform Task Execution	2016	SIGMOD	5.0498046e-05

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