| 5,173 |
FiGO: Fine-Grained Query Optimization in Video Analytics |
2022 |
SIGMOD |
5.6447253e-05 |
| 5,258 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
5.5998705e-05 |
| 5,334 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5649836e-05 |
| 5,337 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5635208e-05 |
| 5,368 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
5.5457532e-05 |
| 5,423 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5130233e-05 |
| 5,509 |
Can Large Language Models Predict Data Correlations from Column Names? |
2023 |
VLDB |
5.4703368e-05 |
| 5,622 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
5.4060403e-05 |
| 5,640 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3933314e-05 |
| 5,645 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
5.3923454e-05 |
| 5,671 |
LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems |
2022 |
SIGMOD |
5.3803919e-05 |
| 5,832 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
5.3111109e-05 |
| 5,861 |
Machine Learning for Databases |
2021 |
VLDB |
5.298883e-05 |
| 5,880 |
COMPASS: Online Sketch-based Query Optimization for In-Memory Databases |
2021 |
SIGMOD |
5.2898074e-05 |
| 5,930 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
5.2682075e-05 |
| 5,952 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2591691e-05 |
| 6,040 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2412035e-05 |
| 6,233 |
Mosaic: A Sample-Based Database System for Open World Query Processing |
2020 |
CIDR |
5.1451876e-05 |
| 6,297 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1227886e-05 |
| 6,328 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
5.1082882e-05 |
| 6,368 |
Pre-training Summarization Models of Structured Datasets for Cardinality Estimation |
2022 |
VLDB |
5.0937722e-05 |
| 6,383 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
5.0884322e-05 |
| 6,519 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
5.0316686e-05 |
| 6,667 |
Leveraging Query Logs and Machine Learning for Parametric Query Optimization |
2022 |
VLDB |
4.9688874e-05 |
| 6,775 |
A Unified Transferable Model for ML-Enhanced DBMS |
2022 |
CIDR |
4.9299192e-05 |
| 6,862 |
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges |
2023 |
VLDB |
4.9051979e-05 |
| 6,879 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
4.8971368e-05 |
| 6,885 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
4.895386e-05 |
| 7,008 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
4.8643538e-05 |
| 7,011 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
4.8629458e-05 |
| 7,123 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
4.8251036e-05 |
| 7,186 |
LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries |
2024 |
SIGMOD |
4.8063731e-05 |
| 7,221 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
4.797194e-05 |
| 7,296 |
Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities |
2022 |
SIGMOD |
4.7723197e-05 |
| 7,330 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
4.7609373e-05 |
| 7,336 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7599411e-05 |
| 7,457 |
Selectivity Functions of Range Queries are Learnable* |
2022 |
SIGMOD |
4.7247191e-05 |
| 7,461 |
Scalable Multi-Query Execution using Reinforcement Learning |
2021 |
SIGMOD |
4.723898e-05 |
| 7,467 |
Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guarantees |
2025 |
SIGMOD |
4.7218691e-05 |
| 7,486 |
Quantum-Inspired Digital Annealing for Join Ordering |
2024 |
VLDB |
4.7180617e-05 |
| 7,655 |
Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward |
2021 |
VLDB |
4.6872456e-05 |
| 7,753 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
4.660151e-05 |
| 7,828 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
4.6407986e-05 |
| 7,854 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
4.6350172e-05 |
| 7,869 |
SALI: A Scalable Adaptive Learned Index Framework based on Probability Models |
2023 |
SIGMOD |
4.6315248e-05 |
| 7,989 |
RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems |
2025 |
VLDB |
4.6124681e-05 |
| 7,990 |
Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD |
2024 |
VLDB |
4.6117441e-05 |
| 8,020 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6040862e-05 |
| 8,026 |
ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning |
2023 |
VLDB |
4.6030518e-05 |
| 8,047 |
Thrifty Query Execution via Incrementability |
2020 |
SIGMOD |
4.5983505e-05 |