| 4,842 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.8826802e-05 |
| 4,909 |
A Method for Optimizing Opaque Filter Queries |
2020 |
SIGMOD |
5.8338804e-05 |
| 4,913 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
5.8316231e-05 |
| 5,125 |
The Art of Balance: A RateupDBTM Experience of Building a CPU/GPU Hybrid Database Product |
2021 |
VLDB |
5.679423e-05 |
| 5,258 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
5.5998705e-05 |
| 5,314 |
Can Learned Models Replace Hash Functions? |
2023 |
VLDB |
5.5724608e-05 |
| 5,337 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5635208e-05 |
| 5,401 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
5.5285035e-05 |
| 5,423 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5130233e-05 |
| 5,469 |
Learned Cardinality Estimation for Similarity Queries |
2021 |
SIGMOD |
5.4898192e-05 |
| 5,622 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
5.4060403e-05 |
| 5,633 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
5.4011156e-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 |
| 5,861 |
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 |
| 5,924 |
HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning |
2023 |
VLDB |
5.2719183e-05 |
| 5,930 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
5.2682075e-05 |
| 5,942 |
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models |
2022 |
SIGMOD |
5.2634242e-05 |
| 5,952 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2591691e-05 |
| 5,972 |
SafeBound: A Practical System for Generating Cardinality Bounds |
2023 |
SIGMOD |
5.2474768e-05 |
| 6,040 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
5.2412035e-05 |
| 6,230 |
Learned Approximate Query Processing: Make it Light, Accurate and Fast |
2021 |
CIDR |
5.145989e-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 |
| 6,456 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
5.0564619e-05 |
| 6,493 |
Joins on Samples: A Theoretical Guide for Practitioners |
2020 |
VLDB |
5.0424713e-05 |
| 6,519 |
Expand your Training Limits! Generating Training Data for ML-based Data Management |
2021 |
SIGMOD |
5.0316686e-05 |
| 6,685 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
4.9627485e-05 |
| 6,714 |
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks |
2024 |
SIGMOD |
4.9512171e-05 |
| 6,750 |
Breaking It Down: An In-depth Study of Index Advisors |
2024 |
VLDB |
4.9392771e-05 |
| 6,775 |
A Unified Transferable Model for ML-Enhanced DBMS |
2022 |
CIDR |
4.9299192e-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 |
| 6,969 |
LpBound: Pessimistic Cardinality Estimation using ℓp-Norms of Degree Sequences |
2025 |
SIGMOD |
4.8799937e-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 |
| 7,126 |
Debunking the Myth of Join Ordering: Toward Robust SQL Analytics |
2025 |
SIGMOD |
4.8232367e-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 |
| 7,251 |
Learning to Sample: Counting with Complex Queries |
2020 |
VLDB |
4.7890519e-05 |
| 7,296 |
Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities |
2022 |
SIGMOD |
4.7723197e-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 |
| 7,474 |
Cardinality Estimation of Approximate Substring Queries using Deep Learning |
2022 |
VLDB |
4.7194345e-05 |
| 7,486 |
Quantum-Inspired Digital Annealing for Join Ordering |
2024 |
VLDB |
4.7180617e-05 |
| 7,610 |
Learning to be a Statistician: Learned Estimator for Number of Distinct Values |
2022 |
VLDB |
4.6965039e-05 |