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Modeling Shifting Workloads for Learned Database Systems

Summary: Online replay buffer management builds a concise model of shifting workloads. Adapts rapidly to skew and correlations, mitigates out-of-distribution inputs, and improves learned cardinality/cost predictions, validated across diverse data domains and workload shifts. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6848
Venue
SIGMOD
Year
2024
Pagerank
4.7049893e-05
Overall Rank
7,564 | 47.44%
DOI
10.1145/3639293

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank Citing Paper Year Venue Pagerank
8,365 NeurDB: On the Design and Implementation of an AI-powered Autonomous Database 2025 CIDR 4.5305127e-05
9,350 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 4.3494621e-05
9,581 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 4.3186744e-05
9,811 A Practical Theory of Generalization in Selectivity Learning 2025 VLDB 4.2742278e-05
10,038 Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] 2026 SIGMOD 4.1905499e-05
10,282 Toward Drift-Aware Database Benchmarking 2026 VLDB 4.1905499e-05
10,300 TATA: An Efficient Framework for Task Transfer in Query Plan Representation 2026 VLDB 4.1905499e-05
10,627 Data-Agnostic Cardinality Learning from Imperfect Workloads 2025 VLDB 4.1905499e-05
10,638 Conformal Prediction for Verifiable Learned Query Optimization 2025 VLDB 4.1905499e-05
10,863 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 4.1905499e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 32 of 32 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 0.00059446482
100 On the Propagation of Errors in the Size of Join Results 1991 SIGMOD 0.00050033475
183 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036859633
203 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00034868567
212 Join Synopses for Approximate Query Answering 1999 SIGMOD 0.00033997204
329 Neo: A Learned Query Optimizer 2019 VLDB 0.00027301488
510 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021420477
512 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00021385343
527 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00020862475
606 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00019251186
634 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00018844568
779 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00016719473
786 Exploiting Statistics on Query Expressions for Optimization 2002 SIGMOD 0.00016624743
804 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001643674
876 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00015660534
905 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00015423174
1,464 Learning Multi-dimensional Indexes 2020 SIGMOD 0.0001184772
2,080 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 9.5954034e-05
2,136 SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads 2003 VLDB 9.4668797e-05
2,937 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 7.8552033e-05
3,144 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 7.4844943e-05
3,269 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 7.3026051e-05
3,580 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 6.9460425e-05
3,623 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 6.9017341e-05
3,924 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 6.6227223e-05
4,413 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 6.1989918e-05
4,592 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 6.056004e-05
4,800 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 5.9077188e-05
5,528 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 5.4571136e-05
5,944 SAM: Database Generation from Query Workloads with Supervised Autoregressive Models 2022 SIGMOD 5.2583712e-05
6,766 Workload-Aware Indexing of Continuously Moving Objects 2009 VLDB 4.9280867e-05
6,860 Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data 2023 SIGMOD 4.9008421e-05
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6,687 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 4.957987e-05
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1,699 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00010848882
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