| 5,168 |
FiGO: Fine-Grained Query Optimization in Video Analytics |
2022 |
SIGMOD |
5.6446115e-05 |
| 5,250 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
5.6007779e-05 |
| 5,339 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5596755e-05 |
| 5,343 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5582234e-05 |
| 5,373 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
5.5410059e-05 |
| 5,412 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5200608e-05 |
| 5,506 |
Can Large Language Models Predict Data Correlations from Column Names? |
2023 |
VLDB |
5.4711611e-05 |
| 5,528 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
5.4571136e-05 |
| 5,630 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
5.4010111e-05 |
| 5,654 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3882121e-05 |
| 5,682 |
LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems |
2022 |
SIGMOD |
5.3752251e-05 |
| 5,787 |
Machine Learning for Databases |
2021 |
VLDB |
5.3256401e-05 |
| 5,844 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
5.3060581e-05 |
| 5,886 |
COMPASS: Online Sketch-based Query Optimization for In-Memory Databases |
2021 |
SIGMOD |
5.2847297e-05 |
| 5,931 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
5.2632167e-05 |
| 5,941 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2594013e-05 |
| 5,994 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2367998e-05 |
| 6,230 |
Mosaic: A Sample-Based Database System for Open World Query Processing |
2020 |
CIDR |
5.1402482e-05 |
| 6,298 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1182917e-05 |
| 6,328 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
5.1034426e-05 |
| 6,365 |
Pre-training Summarization Models of Structured Datasets for Cardinality Estimation |
2022 |
VLDB |
5.0892829e-05 |
| 6,382 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
5.0835686e-05 |
| 6,507 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
5.0273414e-05 |
| 6,639 |
Leveraging Query Logs and Machine Learning for Parametric Query Optimization |
2022 |
VLDB |
4.976781e-05 |
| 6,774 |
A Unified Transferable Model for ML-Enhanced DBMS |
2022 |
CIDR |
4.9253635e-05 |
| 6,860 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
4.9008421e-05 |
| 6,862 |
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges |
2023 |
VLDB |
4.9004921e-05 |
| 6,883 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
4.8918682e-05 |
| 7,009 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
4.8597992e-05 |
| 7,011 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
4.8583284e-05 |
| 7,118 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
4.8204951e-05 |
| 7,188 |
LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries |
2024 |
SIGMOD |
4.8017628e-05 |
| 7,220 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
4.7926382e-05 |
| 7,291 |
Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities |
2022 |
SIGMOD |
4.7677422e-05 |
| 7,326 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
4.7563708e-05 |
| 7,332 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7553758e-05 |
| 7,442 |
Selectivity Functions of Range Queries are Learnable* |
2022 |
SIGMOD |
4.7248554e-05 |
| 7,461 |
Scalable Multi-Query Execution using Reinforcement Learning |
2021 |
SIGMOD |
4.7193668e-05 |
| 7,465 |
Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guarantees |
2025 |
SIGMOD |
4.7186055e-05 |
| 7,486 |
Quantum-Inspired Digital Annealing for Join Ordering |
2024 |
VLDB |
4.7135369e-05 |
| 7,564 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
4.7049893e-05 |
| 7,652 |
Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward |
2021 |
VLDB |
4.6831938e-05 |
| 7,742 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
4.6585812e-05 |
| 7,809 |
Sibyl: Forecasting Time-Evolving Query Workloads |
2024 |
SIGMOD |
4.6415559e-05 |
| 7,854 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
4.6306186e-05 |
| 7,870 |
SALI: A Scalable Adaptive Learned Index Framework based on Probability Models |
2023 |
SIGMOD |
4.6271153e-05 |
| 7,993 |
RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems |
2025 |
VLDB |
4.6080455e-05 |
| 7,994 |
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD |
2024 |
VLDB |
4.607322e-05 |
| 8,003 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6049527e-05 |
| 8,027 |
ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning |
2023 |
VLDB |
4.5986382e-05 |