| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011049779 |
| 2,121 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5017232e-05 |
| 2,762 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1585394e-05 |
| 2,783 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1293383e-05 |
| 3,266 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
7.3074684e-05 |
| 3,348 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1904529e-05 |
| 3,449 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
7.0824319e-05 |
| 3,499 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
7.0376445e-05 |
| 4,417 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
6.2037371e-05 |
| 4,434 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.1929999e-05 |
| 4,543 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
6.1011198e-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 |
| 5,640 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3933314e-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 |
| 6,328 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
5.1082882e-05 |
| 6,383 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
5.0884322e-05 |
| 6,685 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
4.9627485e-05 |
| 6,750 |
Breaking It Down: An In-depth Study of Index Advisors |
2024 |
VLDB |
4.9392771e-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 |
| 7,042 |
LMSFC: A Novel Multidimensional Index based on Learned Monotonic Space Filling Curves |
2023 |
VLDB |
4.8541986e-05 |
| 7,123 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
4.8251036e-05 |
| 7,221 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
4.797194e-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 |
| 7,610 |
Learning to be a Statistician: Learned Estimator for Number of Distinct Values |
2022 |
VLDB |
4.6965039e-05 |
| 7,655 |
Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward |
2021 |
VLDB |
4.6872456e-05 |
| 7,854 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
4.6350172e-05 |
| 8,009 |
CAMAL: Optimizing LSM-trees via Active Learning |
2024 |
SIGMOD |
4.6066863e-05 |
| 8,220 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
4.5557328e-05 |
| 8,448 |
PARQO: Penalty-Aware Robust Plan Selection in Query Optimization |
2024 |
VLDB |
4.5100508e-05 |
| 8,636 |
WISK: A Workload-aware Learned Index for Spatial Keyword Queries |
2023 |
SIGMOD |
4.4801284e-05 |
| 8,650 |
HAP: An Efficient Hamming Space Index Based on Augmented Pigeonhole Principle |
2022 |
SIGMOD |
4.4761716e-05 |
| 8,854 |
Optimizing the cloud? Don't train models. Build oracles! |
2024 |
CIDR |
4.4349047e-05 |
| 8,948 |
One Seed, Two Birds: A Unified Learned Structure for Exact and Approximate Counting |
2024 |
SIGMOD |
4.423786e-05 |
| 9,213 |
PACE: Poisoning Attacks on Learned Cardinality Estimation |
2024 |
SIGMOD |
4.3721075e-05 |
| 9,621 |
ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation |
2023 |
VLDB |
4.3167167e-05 |
| 9,662 |
Efficient Query Re-optimization with Judicious Subquery Selections |
2023 |
SIGMOD |
4.3097631e-05 |
| 9,747 |
Still Asking: How Good Are Query Optimizers, Really? |
2025 |
VLDB |
4.2897489e-05 |
| 9,845 |
Path-centric Cardinality Estimation for Subgraph Matching |
2025 |
VLDB |
4.2721228e-05 |
| 9,852 |
Machine Unlearning in Learned Databases: An Experimental Analysis |
2024 |
SIGMOD |
4.2714575e-05 |
| 9,877 |
Color: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation |
2025 |
VLDB |
4.2656547e-05 |
| 9,878 |
PRICE: A Pretrained Model for Cross-Database Cardinality Estimation |
2025 |
VLDB |
4.2656547e-05 |
| 9,960 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
4.2294678e-05 |
| 10,014 |
BEE: Towards Redundancy Reduction via Block-Separator Decomposition for Subgraph Matching |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,038 |
Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,125 |
Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,149 |
CorrBound: Cardinality Estimation Accounting for Inter- and Intra-relation Correlations |
2026 |
SIGMOD |
4.1945683e-05 |