| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 752 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00017138049 |
| 804 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.0001643674 |
| 905 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423174 |
| 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 |
| 1,978 |
Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses |
2018 |
VLDB |
9.8764627e-05 |
| 2,143 |
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities |
2019 |
SIGMOD |
9.4437798e-05 |
| 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 |
| 2,781 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1282042e-05 |
| 2,971 |
Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models |
2017 |
VLDB |
7.7935535e-05 |
| 3,269 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
7.3026051e-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 |
| 3,516 |
Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs |
2022 |
VLDB |
7.018912e-05 |
| 3,706 |
Every Row Counts: Combining Sketches and Sampling for Accurate Group-By Result Estimates |
2019 |
CIDR |
6.8232992e-05 |
| 3,827 |
Correlation Sketches for Approximate Join-Correlation Queries |
2021 |
SIGMOD |
6.7195959e-05 |
| 3,886 |
Density-optimized Intersection-free Mapping and Matrix Multiplication for Join-Project Operations |
2022 |
VLDB |
6.6610704e-05 |
| 3,924 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
6.6227223e-05 |
| 3,955 |
Efficiently Approximating Selectivity Functions using Low Overhead Regression Models |
2020 |
VLDB |
6.5895015e-05 |
| 3,992 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
6.5519369e-05 |
| 4,352 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
6.2542257e-05 |
| 4,526 |
Simplicity Done Right for Join Ordering |
2021 |
CIDR |
6.1079584e-05 |
| 4,543 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
6.0953507e-05 |
| 4,689 |
Scalable Reservoir Sampling on Many-Core CPUs |
2019 |
SIGMOD |
5.9899401e-05 |
| 4,835 |
MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions |
2019 |
SIGMOD |
5.8861112e-05 |
| 5,886 |
COMPASS: Online Sketch-based Query Optimization for In-Memory Databases |
2021 |
SIGMOD |
5.2847297e-05 |
| 5,931 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
5.2632167e-05 |
| 6,481 |
Joins on Samples: A Theoretical Guide for Practitioners |
2020 |
VLDB |
5.039683e-05 |
| 6,705 |
Combining Sampling and Synopses with Worst-Case Optimal Runtime and Quality Guarantees for Graph Pattern Cardinality Estimation |
2021 |
SIGMOD |
4.9507418e-05 |
| 6,901 |
Disclosure-Compliant Query Answering |
2024 |
SIGMOD |
4.8878659e-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 |
| 7,571 |
Identifying Insufficient Data Coverage in Databases with Multiple Relations |
2020 |
VLDB |
4.7037322e-05 |
| 7,854 |
dbET: Execution Time Distribution-based Plan Selection |
2023 |
SIGMOD |
4.6306186e-05 |
| 8,049 |
Thrifty Query Execution via Incrementability |
2020 |
SIGMOD |
4.5939412e-05 |
| 8,130 |
Robust Query Processing: Mission Possible |
2020 |
VLDB |
4.5746794e-05 |
| 8,345 |
alpha to omega: The Greek Alphabet of Sampling |
2020 |
CIDR |
4.5361313e-05 |
| 9,387 |
Small Selectivities Matter: Lifting the Burden of Empty Samples |
2021 |
SIGMOD |
4.341968e-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 |
| 9,959 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
4.2254157e-05 |
| 10,149 |
CorrBound: Cardinality Estimation Accounting for Inter- and Intra-relation Correlations |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,197 |
Qualitative Join Discovery in Data Lakes using Examples |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,863 |
Graph Transformers for Query Plan Representation: Potentials and Challenges |
2025 |
VLDB |
4.1905499e-05 |
| 11,059 |
Agile-Ant: Self-managing Distributed Cache Management for Cost Optimization of Big Data Applications |
2024 |
VLDB |
4.1905499e-05 |
| 11,087 |
Presto’s History-based Query Optimizer |
2024 |
VLDB |
4.1905499e-05 |
| 11,343 |
Juggler: Autonomous Cost Optimization and Performance Prediction of Big Data Applications |
2022 |
SIGMOD |
4.1905499e-05 |