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: 6847
Venue: SIGMOD
Year: 2024
Pagerank: 4.6407986e-05
Overall Rank: 7,828 | 45.55%
DOI: 10.1145/3639293

Incoming Non-self Citations Over Time

Authors

1. Peizhi Wu
2. Zachary G. Ives

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,688	NeurDB: On the Design and Implementation of an AI-powered Autonomous Database	2025	CIDR	4.4673127e-05
9,345	LIMAO: A Framework for Lifelong Modular Learned Query Optimization	2025	VLDB	4.3536343e-05
9,587	Low Rank Learning for Offline Query Optimization	2025	SIGMOD	4.3215645e-05
9,812	A Practical Theory of Generalization in Selectivity Learning	2025	VLDB	4.2783272e-05
10,038	Understanding Robustness Issues of Updatable Learned Indexes: [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
10,619	Data-Agnostic Cardinality Learning from Imperfect Workloads	2025	VLDB	4.1945683e-05
10,630	Conformal Prediction for Verifiable Learned Query Optimization	2025	VLDB	4.1945683e-05
10,859	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	4.1945683e-05

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	0.00059038975
99	On the Propagation of Errors in the Size of Join Results	1991	SIGMOD	0.00050022914
183	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036721403
204	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00034784455
211	Join Synopses for Approximate Query Answering	1999	SIGMOD	0.00033981214
333	Neo: A Learned Query Optimizer	2019	VLDB	0.00027206884
512	STHoles: A Multidimensional Workload-Aware Histogram	2001	SIGMOD	0.00021380733
514	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.0002124895
529	Self-tuning Histograms: Building Histograms Without Looking at Data	1999	SIGMOD	0.00020828852
608	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00019235898
640	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00018759152
782	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00016729063
790	Exploiting Statistics on Query Expressions for Optimization	2002	SIGMOD	0.0001663283
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
910	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00015423056
1,478	Learning Multi-dimensional Indexes	2020	SIGMOD	0.00011762542
2,083	Towards a Learning Optimizer for Shared Clouds	2019	VLDB	9.5834572e-05
2,137	SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads	2003	VLDB	9.4719326e-05
2,985	DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems	2021	VLDB	7.7795847e-05
3,142	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	7.4815444e-05
3,266	Learned Cardinality Estimation: An In-depth Study	2022	SIGMOD	7.3074684e-05
3,580	Query Performance Prediction for Concurrent Queries using Graph Embedding	2020	VLDB	6.9500996e-05
3,625	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	6.9055212e-05
3,924	A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation	2021	SIGMOD	6.6271553e-05
4,417	Robust Query Driven Cardinality Estimation under Changing Workloads	2023	VLDB	6.2037371e-05
4,593	Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift	2023	SIGMOD	6.0606891e-05
4,804	Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload	2021	SIGMOD	5.910467e-05
5,645	Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts	2022	SIGMOD	5.3923454e-05
5,942	SAM: Database Generation from Query Workloads with Supervised Autoregressive Models	2022	SIGMOD	5.2634242e-05
6,819	Workload-Aware Indexing of Continuously Moving Objects	2009	VLDB	4.9158166e-05
6,879	Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data	2023	SIGMOD	4.8971368e-05

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6,685	How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks	2025	SIGMOD	4.9627485e-05
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5,671	LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems	2022	SIGMOD	5.3803919e-05
7,753	Rethinking Learned Cost Models: Why Start from Scratch?	2023	SIGMOD	4.660151e-05
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