| 318 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021167555 |
| 406 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00019045544 |
| 430 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00018409112 |
| 512 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017050173 |
| 982 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00012714044 |
| 1,064 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00012202282 |
| 1,132 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011898257 |
| 1,156 |
QuickSel: Quick Selectivity Learning with Mixture Models |
2020 |
SIGMOD |
0.00011777105 |
| 1,199 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011563985 |
| 1,580 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
0.00010180835 |
| 1,734 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.7545773e-05 |
| 2,004 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.2065719e-05 |
| 2,210 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.8257742e-05 |
| 2,342 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
8.6060437e-05 |
| 2,522 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.3477168e-05 |
| 2,583 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
8.2589758e-05 |
| 2,765 |
Instance-Optimized Data Layouts for Cloud Analytics Workloads |
2021 |
SIGMOD |
8.0439015e-05 |
| 2,834 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
7.9560627e-05 |
| 2,846 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
7.9453616e-05 |
| 2,908 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.8742664e-05 |
| 3,052 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
7.7052471e-05 |
| 3,160 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
7.5807496e-05 |
| 3,210 |
Efficiently Approximating Selectivity Functions using Low Overhead Regression Models |
2020 |
VLDB |
7.5363533e-05 |
| 3,327 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.4207879e-05 |
| 3,486 |
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements |
2022 |
SIGMOD |
7.2636102e-05 |
| 3,562 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
7.2046519e-05 |
| 3,741 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
7.0594076e-05 |
| 3,965 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.8918628e-05 |
| 4,258 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.6994722e-05 |
| 4,311 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
6.6727978e-05 |
| 4,457 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.5913732e-05 |
| 4,563 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
6.5320994e-05 |
| 4,741 |
Machine Learning for Databases |
2021 |
VLDB |
6.4410027e-05 |
| 4,950 |
Debunking the Myth of Join Ordering: Toward Robust SQL Analytics |
2025 |
SIGMOD |
6.3421691e-05 |
| 5,003 |
COMPASS: Online Sketch-based Query Optimization for In-Memory Databases |
2021 |
SIGMOD |
6.3188773e-05 |
| 5,022 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
6.3100988e-05 |
| 5,039 |
Leveraging Query Logs and Machine Learning for Parametric Query Optimization |
2022 |
VLDB |
6.3023214e-05 |
| 5,110 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
6.269351e-05 |
| 5,126 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
6.261175e-05 |
| 5,449 |
High-Performance Row Pattern Recognition Using Joins |
2023 |
VLDB |
6.1254016e-05 |
| 5,649 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.052326e-05 |
| 5,840 |
Pre-training Summarization Models of Structured Datasets for Cardinality Estimation |
2022 |
VLDB |
5.9737703e-05 |
| 6,660 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.7178404e-05 |
| 6,673 |
Index-Accelerated Pattern Matching in Event Stores |
2021 |
SIGMOD |
5.7131264e-05 |
| 6,710 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
5.7019157e-05 |
| 6,753 |
Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities |
2022 |
SIGMOD |
5.6904085e-05 |
| 6,818 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
5.672718e-05 |
| 7,332 |
Selectivity Functions of Range Queries are Learnable* |
2022 |
SIGMOD |
5.5499953e-05 |
| 7,341 |
Scalable Multi-Query Execution using Reinforcement Learning |
2021 |
SIGMOD |
5.5481233e-05 |
| 7,363 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
5.5418564e-05 |