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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
6909
Venue
SIGMOD
Year
2024
Pagerank
5.9141228e-05
Overall Rank
6,323 | 56.62%
DOI
10.1145/3639293

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wu_sigmod24,
        title = {{Modeling Shifting Workloads for Learned Database Systems}},
        author = {Wu, Peizhi and Ives, Zachary G.},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3639293},
        url = {https://dl.acm.org/doi/10.1145/3639293},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 13 of 13 citing papers.

Rank Citing Paper Year Venue Pagerank
8,221 NeurDB: On the Design and Implementation of an AI-powered Autonomous Database 2025 CIDR 5.4640314e-05
9,428 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.2709145e-05
9,599 Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] 2026 SIGMOD 5.2492748e-05
9,601 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 5.2487799e-05
9,958 A Practical Theory of Generalization in Selectivity Learning 2025 VLDB 5.1879626e-05
10,033 Modeling Concurrency Control as a Learnable Function 2026 SIGMOD 5.173224e-05
10,196 Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] 2026 SIGMOD 5.093636e-05
10,272 NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis] 2026 SIGMOD 5.093636e-05
10,569 Toward Drift-Aware Database Benchmarking 2026 VLDB 5.093636e-05
10,586 TATA: An Efficient Framework for Task Transfer in Query Plan Representation 2026 VLDB 5.093636e-05
10,875 Data-Agnostic Cardinality Learning from Imperfect Workloads 2025 VLDB 5.093636e-05
10,884 Conformal Prediction for Verifiable Learned Query Optimization 2025 VLDB 5.093636e-05
11,083 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 5.093636e-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
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
89 On the Propagation of Errors in the Size of Join Results 1991 SIGMOD 0.00035031529
136 Join Synopses for Approximate Query Answering 1999 SIGMOD 0.00030123303
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
334 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00020875082
365 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00020041735
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
448 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00018292618
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
498 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00017440583
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
664 Exploiting Statistics on Query Expressions for Optimization 2002 SIGMOD 0.00015167825
873 Learning Multi-dimensional Indexes 2020 SIGMOD 0.00013481915
1,468 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010686496
1,832 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 9.6607418e-05
2,121 SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads 2003 VLDB 9.1402718e-05
2,313 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.762627e-05
2,543 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.4445934e-05
2,812 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 8.0979597e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
3,086 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7708642e-05
3,338 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.5068221e-05
3,809 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.1074195e-05
3,953 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.996368e-05
4,612 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 6.6072026e-05
5,105 SAM: Database Generation from Query Workloads with Supervised Autoregressive Models 2022 SIGMOD 6.3628539e-05
6,132 Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data 2023 SIGMOD 5.9660278e-05
6,458 Workload-Aware Indexing of Continuously Moving Objects 2009 VLDB 5.8736158e-05
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