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Plan-Structured Deep Neural Network Models for Query Performance Prediction

Summary: Plan-structured deep neural networks that mirror optimizer plans to predict query latency. No hand-crafted features; learns operator-input interactions, adapts to workloads, with training optimizations, achieving state-of-the-art performance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12049
Venue
VLDB
Year
2019
Pagerank
0.0001650812
Overall Rank
563 | 96.14%
DOI
10.14778/3342263.3342646

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{marcus_vldb19,
        title = {{Plan-Structured Deep Neural Network Models for Query Performance Prediction}},
        author = {Marcus, Ryan and Papaemmanouil, Olga},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {11},
        pages = {1733--1746},
        doi = {10.14778/3342263.3342646},
        url = {https://doi.org/10.14778/3342263.3342646},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 50 of 75 citing papers.

Rank Citing Paper Year Venue Pagerank
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
1,481 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010644613
1,551 Updatable Learned Index with Precise Positions 2021 VLDB 0.00010381398
1,832 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 9.6607418e-05
2,313 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.762627e-05
2,355 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7022189e-05
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,452 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5584e-05
2,762 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1539867e-05
2,812 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 8.0979597e-05
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
2,888 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.9941489e-05
2,991 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 7.8880723e-05
3,213 Estimating Cardinalities with Deep Sketches 2019 SIGMOD 7.6328677e-05
3,586 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.2834069e-05
3,662 openGauss: An Autonomous Database System 2021 VLDB 7.2166682e-05
3,809 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.1074195e-05
3,961 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.987575e-05
4,073 Stable Learned Bloom Filters for Data Streams 2020 VLDB 6.9242783e-05
4,434 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7079088e-05
4,468 One Model to Rule them All: Towards Zero-Shot Learning for Databases 2022 CIDR 6.6819041e-05
4,643 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.5907466e-05
5,011 Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach 2020 SIGMOD 6.4020848e-05
5,073 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.3751266e-05
5,107 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.3623786e-05
5,340 Machine Learning for Databases 2021 VLDB 6.2603359e-05
5,388 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 6.2362811e-05
5,558 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.1749098e-05
5,573 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1682747e-05
5,767 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.0945741e-05
6,024 Expand your Training Limits! Generating Training Data for ML-based Data Management 2021 SIGMOD 6.0031118e-05
6,088 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 5.9813965e-05
6,323 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9141228e-05
6,357 A Unified Transferable Model for ML-Enhanced DBMS 2022 CIDR 5.9020843e-05
6,921 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.7388557e-05
7,076 Refactoring Index Tuning Process with Benefit Estimation 2024 VLDB 5.7098893e-05
7,589 Cost-Intelligent Data Analytics in the Cloud 2024 CIDR 5.5907048e-05
7,755 Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD 2024 VLDB 5.5519655e-05
7,757 CAMAL: Optimizing LSM-trees via Active Learning 2024 SIGMOD 5.5508469e-05
7,785 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.5450355e-05
7,846 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.5331459e-05
8,163 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 5.4751517e-05
8,410 Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems 2022 VLDB 5.4309397e-05
8,572 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 5.4102362e-05
8,783 Tiresias: Enabling Predictive Autonomous Storage and Indexing 2022 VLDB 5.3740362e-05
8,984 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.3395569e-05
9,224 Phoebe: A Learning-based Checkpoint Optimizer 2021 VLDB 5.3035811e-05
9,428 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.2709145e-05
9,465 Are Joins over LSM-trees Ready? Take RocksDB as an Example 2025 VLDB 5.2634238e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 21 of 21 cited papers.

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

Rank Cited Paper Year Venue Pagerank
23 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.00054886415
43 The Case for Learned Index Structures 2018 SIGMOD 0.00046060254
53 Eddies: Continuously Adaptive Query Processing 2000 SIGMOD 0.00041071971
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
100 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034385207
176 Deep Learning for Entity Matching: A Design Space Exploration 2018 SIGMOD 0.00027191081
234 Self-Driving Database Management Systems 2017 CIDR 0.00023810722
388 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019410042
448 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00018292618
476 The Making of TPC-DS 2006 VLDB 0.00017860667
566 Towards a Robust Query Optimizer: A Principled and Practical Approach 2005 SIGMOD 0.00016436005
624 Performance Prediction for Concurrent Database Workloads 2011 SIGMOD 0.00015683402
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00015014887
1,143 Toward a Progress Indicator for Database Queries 2004 SIGMOD 0.00011999403
1,665 ParaTimer: A Progress Indicator for MapReduce DAGs 2010 SIGMOD 0.00010069173
2,404 Cardinality Estimation Using Sample Views with Quality Assurance 2007 SIGMOD 8.6225576e-05
2,589 Statistical Learning Techniques for Costing XML Queries 2005 VLDB 8.371643e-05
2,958 WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases 2016 VLDB 7.9197796e-05
3,051 Towards a Hands-Free Query Optimizer through Deep Learning 2019 CIDR 7.8121919e-05
3,482 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.3751635e-05
6,138 Operator and Query Progress Estimation in Microsoft SQL Server Live Query Statistics 2016 SIGMOD 5.9640712e-05
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