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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
h16a47590c7258e4a
Venue
SIGMOD
Year
2024
Pagerank
5.9697921e-05
Overall Rank
5,850 | 60.69%
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 15 of 15 citing papers.

Rank Citing Paper Year Venue Pagerank
8,032 NeurDB: On the Design and Implementation of an AI-powered Autonomous Database 2025 CIDR 5.4017794e-05
8,638 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.2965922e-05
8,969 Conformal Prediction for Verifiable Learned Query Optimization 2025 VLDB 5.2477982e-05
9,635 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 5.1453041e-05
9,723 A Practical Theory of Generalization in Selectivity Learning 2025 VLDB 5.1329654e-05
9,782 Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] 2026 SIGMOD 5.129066e-05
9,962 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 5.1014161e-05
10,230 Modeling Concurrency Control as a Learnable Function 2026 SIGMOD 5.0547568e-05
10,300 Data-Agnostic Cardinality Learning from Imperfect Workloads 2025 VLDB 5.0407989e-05
10,424 Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] 2026 SIGMOD 4.9769913e-05
10,495 NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis] 2026 SIGMOD 4.9769913e-05
10,761 Toward Drift-Aware Database Benchmarking 2026 VLDB 4.9769913e-05
10,808 NeurIDA: Dynamic Modeling for Effective In-Database Analytics 2026 VLDB 4.9769913e-05
10,812 BaCon: Efficient Batch Processing of Counting Queries 2026 VLDB 4.9769913e-05
10,933 TATA: An Efficient Framework for Task Transfer in Query Plan Representation 2026 VLDB 4.9769913e-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
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061067652
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036675568
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035876108
91 On the Propagation of Errors in the Size of Join Results 1991 SIGMOD 0.00034748721
138 Join Synopses for Approximate Query Answering 1999 SIGMOD 0.00029618887
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021276452
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021166957
361 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020000855
371 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019822444
437 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00018310278
454 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00017955913
462 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017836105
510 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017059914
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016408613
644 Exploiting Statistics on Query Expressions for Optimization 2002 SIGMOD 0.00015209065
869 Learning Multi-dimensional Indexes 2020 SIGMOD 0.00013363241
1,432 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010676754
1,515 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 0.00010418766
2,142 SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads 2003 VLDB 8.9644817e-05
2,278 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.7057608e-05
2,342 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.6074783e-05
2,833 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9539771e-05
2,837 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.9495917e-05
3,053 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7041081e-05
3,327 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.4233639e-05
3,565 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.200937e-05
3,979 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.8791817e-05
4,559 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 6.5324275e-05
5,211 SAM: Database Generation from Query Workloads with Supervised Autoregressive Models 2022 SIGMOD 6.2232582e-05
5,715 Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data 2023 SIGMOD 6.0178585e-05
6,582 Workload-Aware Indexing of Continuously Moving Objects 2009 VLDB 5.7421081e-05
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