Database Workload Characterization with Query Plan Encoders

Summary: Proposes pretrained query plan encoders that learn structural and performance features from plans to characterize workloads. Enables transfer learning for latency prediction and plan classification, showing transferable representations and adaptability. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12962
Venue: VLDB
Year: 2022
Pagerank: 5.3979505e-05
Overall Rank: 5,637 | 60.79%
DOI: 10.14778/3503585.3503600

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 11 of 11 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,429	Real-time Workload Pattern Analysis for Large-scale Cloud Databases	2023	VLDB	7.1010535e-05
6,328	A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies	2024	VLDB	5.1082882e-05
7,753	Rethinking Learned Cost Models: Why Start from Scratch?	2023	SIGMOD	4.660151e-05
8,020	The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions	2024	VLDB	4.6040862e-05
9,929	Wred: Workload Reduction for Scalable Index Tuning	2024	SIGMOD	4.2510122e-05
9,930	Wii: Dynamic Budget Reallocation In Index Tuning	2024	SIGMOD	4.2510122e-05
10,125	Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis]	2026	SIGMOD	4.1945683e-05
10,288	TATA: An Efficient Framework for Task Transfer in Query Plan Representation	2026	VLDB	4.1945683e-05
10,301	DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning	2026	VLDB	4.1945683e-05
10,543	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	4.1945683e-05
10,564	PlanRGCN: Predicting SPARQL Query Performance	2025	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
71	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059038975
183	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036721403
424	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00023616398
782	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00016729063
801	SageDB: A Learned Database System	2019	CIDR	0.00016505496
806	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.00016434274
884	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.00015654004
1,019	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00014625603
1,855	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00010315245
3,269	iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases	2019	VLDB	7.2998062e-05
5,010	iTuned: A Tool for Configuring and Visualizing Database Parameters	2010	SIGMOD	5.7611118e-05
7,776	Plan Stitch: Harnessing the Best of Many Plans	2018	VLDB	4.6537231e-05

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Overall Rank	Paper	Year	Venue	Pagerank
7,828	Modeling Shifting Workloads for Learned Database Systems	2024	SIGMOD	4.6407986e-05
2,364	Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries	2020	SIGMOD	8.9554751e-05
3,429	Real-time Workload Pattern Analysis for Large-scale Cloud Databases	2023	VLDB	7.1010535e-05
4,417	Robust Query Driven Cardinality Estimation under Changing Workloads	2023	VLDB	6.2037371e-05
5,473	Facilitating SQL Query Composition and Analysis	2020	SIGMOD	5.4885366e-05
4,661	PreQR: Pre-training Representation for SQL Understanding	2022	SIGMOD	6.0137947e-05
10,859	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	4.1945683e-05
3,580	Query Performance Prediction for Concurrent Queries using Graph Embedding	2020	VLDB	6.9500996e-05
4,549	Database-Agnostic Workload Management	2019	CIDR	6.0926728e-05
884	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.00015654004