| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011050093 |
| 3,269 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
7.3026051e-05 |
| 3,492 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
7.0435484e-05 |
| 3,992 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5519369e-05 |
| 4,413 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
6.1989918e-05 |
| 5,339 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5596755e-05 |
| 5,373 |
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing |
2022 |
VLDB |
5.5410059e-05 |
| 5,405 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
5.5243727e-05 |
| 5,412 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5200608e-05 |
| 5,639 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
5.3972261e-05 |
| 5,654 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3882121e-05 |
| 5,978 |
SafeBound: A Practical System for Generating Cardinality Bounds |
2023 |
SIGMOD |
5.2424396e-05 |
| 6,298 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1182917e-05 |
| 6,382 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
5.0835686e-05 |
| 6,774 |
A Unified Transferable Model for ML-Enhanced DBMS |
2022 |
CIDR |
4.9253635e-05 |
| 7,118 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
4.8204951e-05 |
| 7,220 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
4.7926382e-05 |
| 7,465 |
Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guarantees |
2025 |
SIGMOD |
4.7186055e-05 |
| 7,652 |
Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward |
2021 |
VLDB |
4.6831938e-05 |
| 7,676 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
4.6770108e-05 |
| 7,854 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
4.6306186e-05 |
| 8,219 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
4.551524e-05 |
| 8,585 |
A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning |
2024 |
VLDB |
4.4856045e-05 |
| 8,660 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
4.4680058e-05 |
| 8,711 |
Parachute: Single-Pass Bi-Directional Information Passing |
2025 |
VLDB |
4.4582346e-05 |
| 9,322 |
Are Joins over LSM-trees Ready? Take RocksDB as an Example |
2025 |
VLDB |
4.351469e-05 |
| 9,350 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
4.3494621e-05 |
| 9,487 |
Spatial Query Optimization With Learning |
2024 |
VLDB |
4.3300131e-05 |
| 9,581 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
4.3186744e-05 |
| 9,628 |
Approximate Sketches |
2024 |
SIGMOD |
4.3102157e-05 |
| 9,746 |
Still Asking: How Good Are Query Optimizers, Really? |
2025 |
VLDB |
4.2856385e-05 |
| 9,811 |
A Practical Theory of Generalization in Selectivity Learning |
2025 |
VLDB |
4.2742278e-05 |
| 9,824 |
Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement |
2025 |
SIGMOD |
4.2710095e-05 |
| 9,841 |
Machine Unlearning in Learned Databases: An Experimental Analysis |
2024 |
SIGMOD |
4.2685233e-05 |
| 9,868 |
Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections |
2022 |
VLDB |
4.2634472e-05 |
| 9,916 |
Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes |
2023 |
VLDB |
4.2520778e-05 |
| 9,956 |
How to Optimize SQL Queries? A Comparison Between Split, Holistic, and Hybrid Approaches |
2025 |
VLDB |
4.2332427e-05 |
| 9,959 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
4.2254157e-05 |
| 10,049 |
Approximate Query Processing under Updates |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,112 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,203 |
Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,219 |
Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,225 |
LIO: A lightweight and interpretable query optimizer based on an evolutionary forest |
2026 |
VLDB |
4.1905499e-05 |
| 10,227 |
Sample-based Distinct Cardinality Estimation for Multiple Attributes in Multi-Dataset Queries |
2026 |
VLDB |
4.1905499e-05 |
| 10,271 |
OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning |
2026 |
VLDB |
4.1905499e-05 |
| 10,300 |
TATA: An Efficient Framework for Task Transfer in Query Plan Representation |
2026 |
VLDB |
4.1905499e-05 |
| 10,627 |
Data-Agnostic Cardinality Learning from Imperfect Workloads |
2025 |
VLDB |
4.1905499e-05 |
| 10,635 |
Robust Plan Evaluation based on Approximate Probabilistic Machine Learning |
2025 |
VLDB |
4.1905499e-05 |
| 10,638 |
Conformal Prediction for Verifiable Learned Query Optimization |
2025 |
VLDB |
4.1905499e-05 |
| 10,863 |
Graph Transformers for Query Plan Representation: Potentials and Challenges |
2025 |
VLDB |
4.1905499e-05 |