Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction

Summary: Libra is a transfer-learning framework for DBMS performance prediction that predicts a target context’s parameter-sensitivity profile in one shot, then retrieves the most similar source context. It avoids negative transfer by focusing sampling on high-impact parameters, yielding up to 32x less sampling and large error reductions across 161 contexts. (summarized by gpt-5.4-mini on Apr 12 2026)

Paper ID: h7b3c0865f93b1a62
Venue: VLDB
Year: 2026
Pagerank: 4.9605343e-05
Overall Rank: 11,142 | 25.32%
DOI: 10.14778/3796195.3796207
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

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Authors

1. Tatsuhiro Nakamori (Keio University)
2. Hideyuki Kawashima (Keio University)

BibTeX Citation

@article{nakamori_vldb26,
        title = {{Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction}},
        author = {Nakamori, Tatsuhiro and Kawashima, Hideyuki},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {5},
        pages = {945--957},
        doi = {10.14778/3796195.3796207},
        url = {https://doi.org/10.14778/3796195.3796207},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
74	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00037164008
309	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.0002143249
313	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00021288111
545	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.0001657599
1,294	An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems	2021	VLDB	0.00011129451
2,169	QueryFormer: A Tree Transformer Model for Query Plan Representation	2022	VLDB	8.9151935e-05
2,572	ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases	2021	SIGMOD	8.2581875e-05
2,619	Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction	2022	VLDB	8.1985665e-05
2,779	Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation	2022	VLDB	8.0085666e-05
3,057	LlamaTune: Sample-Efficient DBMS Configuration Tuning	2022	VLDB	7.6865211e-05
4,101	Towards Dynamic and Safe Configuration Tuning for Cloud Databases	2022	SIGMOD	6.7930727e-05
4,833	Stage: Query Execution Time Prediction in Amazon Redshift	2024	SIGMOD	6.3834856e-05
5,304	An Efficient Transfer Learning Based Configuration Adviser for Database Tuning	2024	VLDB	6.1758143e-05
7,483	T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees	2025	SIGMOD	5.5007125e-05
9,195	DBSeer: Pain-free Database Administration through Workload Intelligence	2015	VLDB	5.1958544e-05

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