The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that “Read the Manual”

Summary: Proposes NLP-enhanced database tuning tools that “read” natural-language manuals and Web documents to mine configuration hints. A prototype improves MySQL and PostgreSQL TPC-H performance over defaults, while exposing challenges in extracting and applying tuning knowledge. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: ha94d4ba9bf80f07a
Venue: VLDB
Year: 2021
Pagerank: 5.3671363e-05
Overall Rank: 8,258 | 44.48%
DOI: 10.14778/3450980.3450984

Incoming Non-self Citations Over Time

Authors

1. Immanuel Trummer (Cornell University)

BibTeX Citation

@article{trummer_vldb21,
        title = {{The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that “Read the Manual”}},
        author = {Trummer, Immanuel},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {7},
        pages = {1159--1165},
        doi = {10.14778/3450980.3450984},
        url = {https://doi.org/10.14778/3450980.3450984},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,250	DB-BERT: A Database Tuning Tool that "Reads the Manual"	2022	SIGMOD	0.00011339256
3,250	How Large Language Models Will Disrupt Data Management	2023	VLDB	7.4938546e-05
5,357	Can Large Language Models Predict Data Correlations from Column Names?	2023	VLDB	6.1619918e-05
8,838	Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual	2022	SIGMOD	5.2670355e-05
9,919	Waffle: In-memory Grid Index for Moving Objects with Reinforcement Learning-based Configuration Tuning System	2022	VLDB	5.1103839e-05
10,489	On Self-Designing Learned Indexes	2026	SIGMOD	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1	Access Path Selection in a Relational Database Management System	1979	SIGMOD	0.0023947656
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061066921
78	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036684414
85	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035864347
145	Neo: A Learned Query Optimizer	2019	VLDB	0.0002908188
160	CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies	2004	SIGMOD	0.00027837289
224	Self-Driving Database Management Systems	2017	CIDR	0.00024013745
314	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.00021282642
437	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00018315867
439	ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores	2016	VLDB	0.00018251249
536	NaLIR: An Interactive Natural Language Interface for Querying Relational Databases	2014	SIGMOD	0.00016771934
1,156	QuickSel: Quick Selectivity Learning with Mixture Models	2020	SIGMOD	0.00011777105
1,603	SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning	2019	SIGMOD	0.00010097649
1,706	Algorithms for Materialized View Design in Data Warehousing Environment	1997	VLDB	9.8299851e-05
2,275	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	8.7090584e-05
3,567	On the Complexity of Approximate Query Optimization	2002	PODS	7.2010667e-05
4,188	Schedule Optimization for Data Processing Flows on the Cloud	2011	SIGMOD	6.7464862e-05

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