| 378 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019638121 |
| 1,061 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00012369764 |
| 1,122 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.0001209124 |
| 1,241 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011521639 |
| 1,832 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
9.6607418e-05 |
| 1,876 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.5717543e-05 |
| 1,988 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.3501502e-05 |
| 2,420 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.605257e-05 |
| 2,543 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
8.4445934e-05 |
| 2,620 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
8.3363963e-05 |
| 2,723 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.2049453e-05 |
| 2,731 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
8.1959181e-05 |
| 2,762 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
8.1539867e-05 |
| 2,844 |
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction |
2022 |
VLDB |
8.0608767e-05 |
| 2,991 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
7.8880723e-05 |
| 3,035 |
Instance-Optimized Data Layouts for Cloud Analytics Workloads |
2021 |
SIGMOD |
7.8297746e-05 |
| 3,086 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
7.7708642e-05 |
| 3,338 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.5068221e-05 |
| 3,662 |
openGauss: An Autonomous Database System |
2021 |
VLDB |
7.2166682e-05 |
| 3,688 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
7.201795e-05 |
| 4,011 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
6.959982e-05 |
| 4,349 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
6.7504619e-05 |
| 4,368 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.7393882e-05 |
| 4,434 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.7079088e-05 |
| 4,468 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
6.6819041e-05 |
| 4,470 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
6.6817353e-05 |
| 4,671 |
PreQR: Pre-training Representation for SQL Understanding |
2022 |
SIGMOD |
6.5732787e-05 |
| 4,929 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.4423294e-05 |
| 5,059 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
6.3807509e-05 |
| 5,105 |
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models |
2022 |
SIGMOD |
6.3628539e-05 |
| 5,107 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.3623786e-05 |
| 5,388 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
6.2362811e-05 |
| 5,529 |
Debunking the Myth of Join Ordering: Toward Robust SQL Analytics |
2025 |
SIGMOD |
6.18591e-05 |
| 5,551 |
PGMJoins: Random Join Sampling with Graphical Models |
2021 |
SIGMOD |
6.1782856e-05 |
| 5,576 |
SafeBound: A Practical System for Generating Cardinality Bounds |
2023 |
SIGMOD |
6.1663946e-05 |
| 5,639 |
LpBound: Pessimistic Cardinality Estimation using ℓp-Norms of Degree Sequences |
2025 |
SIGMOD |
6.1385102e-05 |
| 5,712 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.1123894e-05 |
| 5,792 |
Pre-training Summarization Models of Structured Datasets for Cardinality Estimation |
2022 |
VLDB |
6.0871213e-05 |
| 5,974 |
Towards instance-optimized data systems |
2021 |
VLDB |
6.0230488e-05 |
| 6,088 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
5.9813965e-05 |
| 6,132 |
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data |
2023 |
SIGMOD |
5.9660278e-05 |
| 6,271 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
5.9326197e-05 |
| 6,323 |
Modeling Shifting Workloads for Learned Database Systems |
2024 |
SIGMOD |
5.9141228e-05 |
| 6,357 |
A Unified Transferable Model for ML-Enhanced DBMS |
2022 |
CIDR |
5.9020843e-05 |
| 6,462 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
5.8717744e-05 |
| 6,543 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.8461929e-05 |
| 6,704 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
5.797374e-05 |
| 7,076 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
5.7098893e-05 |
| 7,112 |
Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning |
2023 |
VLDB |
5.6990782e-05 |
| 7,193 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
5.6770249e-05 |