| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 503 |
Worst-case Optimal Join Algorithms |
2012 |
PODS |
0.00021517145 |
| 752 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00017138049 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 1,464 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.0001184772 |
| 1,638 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00011050093 |
| 1,699 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010848882 |
| 1,756 |
Sampling-Based Query Re-Optimization |
2016 |
SIGMOD |
0.00010659753 |
| 2,364 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
8.955077e-05 |
| 2,769 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1512848e-05 |
| 2,971 |
Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models |
2017 |
VLDB |
7.7935535e-05 |
| 3,345 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1908499e-05 |
| 3,455 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
7.0760196e-05 |
| 3,492 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
7.0435484e-05 |
| 3,924 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
6.6227223e-05 |
| 3,992 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5519369e-05 |
| 4,129 |
A Statistical Perspective on Discovering Functional Dependencies in Noisy Data |
2020 |
SIGMOD |
6.4208557e-05 |
| 4,352 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
6.2542257e-05 |
| 4,413 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
6.1989918e-05 |
| 4,431 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.1870601e-05 |
| 5,405 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
5.5243727e-05 |
| 5,506 |
Can Large Language Models Predict Data Correlations from Column Names? |
2023 |
VLDB |
5.4711611e-05 |
| 5,941 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2594013e-05 |
| 5,944 |
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models |
2022 |
SIGMOD |
5.2583712e-05 |
| 5,952 |
PGMJoins: Random Join Sampling with Graphical Models |
2021 |
SIGMOD |
5.2547498e-05 |
| 6,365 |
Pre-training Summarization Models of Structured Datasets for Cardinality Estimation |
2022 |
VLDB |
5.0892829e-05 |
| 6,715 |
Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks |
2024 |
SIGMOD |
4.9464666e-05 |
| 9,350 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
4.3494621e-05 |
| 9,662 |
Efficient Query Re-optimization with Judicious Subquery Selections |
2023 |
SIGMOD |
4.3056334e-05 |
| 9,877 |
PRICE: A Pretrained Model for Cross-Database Cardinality Estimation |
2025 |
VLDB |
4.2615675e-05 |
| 9,947 |
SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index |
2026 |
SIGMOD |
4.2391999e-05 |
| 10,047 |
AgentTune: An Agent-Based Large Language Model Framework for Database Knob Tuning |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,149 |
CorrBound: Cardinality Estimation Accounting for Inter- and Intra-relation Correlations |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,271 |
OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning |
2026 |
VLDB |
4.1905499e-05 |
| 10,598 |
ACE: A Cardinality Estimator for Set-Valued Queries |
2025 |
VLDB |
4.1905499e-05 |