| 318 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021166957 |
| 406 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00019050182 |
| 422 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00018488849 |
| 510 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017059914 |
| 981 |
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation |
2022 |
VLDB |
0.00012713454 |
| 1,065 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00012202293 |
| 1,128 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011901941 |
| 1,155 |
QuickSel: Quick Selectivity Learning with Mixture Models |
2020 |
SIGMOD |
0.00011777046 |
| 1,195 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011574218 |
| 1,580 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
0.00010177136 |
| 1,735 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.7566604e-05 |
| 2,002 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.2076835e-05 |
| 2,209 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.8360101e-05 |
| 2,342 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
8.6074783e-05 |
| 2,518 |
Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation |
2022 |
VLDB |
8.3532841e-05 |
| 2,583 |
Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation |
2021 |
VLDB |
8.2589842e-05 |
| 2,765 |
Instance-Optimized Data Layouts for Cloud Analytics Workloads |
2021 |
SIGMOD |
8.0401855e-05 |
| 2,833 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
7.9539771e-05 |
| 2,844 |
FactorJoin: A New Cardinality Estimation Framework for Join Queries |
2023 |
SIGMOD |
7.9446987e-05 |
| 2,908 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.8716173e-05 |
| 3,053 |
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation |
2021 |
SIGMOD |
7.7041081e-05 |
| 3,161 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
7.5771609e-05 |
| 3,208 |
Efficiently Approximating Selectivity Functions using Low Overhead Regression Models |
2020 |
VLDB |
7.5355264e-05 |
| 3,327 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
7.4233639e-05 |
| 3,487 |
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements |
2022 |
SIGMOD |
7.2603973e-05 |
| 3,563 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
7.2023194e-05 |
| 3,742 |
FACE: A Normalizing Flow based Cardinality Estimator |
2022 |
VLDB |
7.0564546e-05 |
| 3,964 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.889374e-05 |
| 4,240 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.7064546e-05 |
| 4,299 |
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads |
2024 |
VLDB |
6.6766173e-05 |
| 4,459 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.5883555e-05 |
| 4,559 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
6.5324275e-05 |
| 4,743 |
Machine Learning for Databases |
2021 |
VLDB |
6.4379536e-05 |
| 4,945 |
Debunking the Myth of Join Ordering: Toward Robust SQL Analytics |
2025 |
SIGMOD |
6.3418058e-05 |
| 5,006 |
COMPASS: Online Sketch-based Query Optimization for In-Memory Databases |
2021 |
SIGMOD |
6.3159614e-05 |
| 5,020 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
6.3096708e-05 |
| 5,042 |
Leveraging Query Logs and Machine Learning for Parametric Query Optimization |
2022 |
VLDB |
6.2995365e-05 |
| 5,110 |
Steering Query Optimizers: A Practical Take on Big Data Workloads |
2021 |
SIGMOD |
6.2678118e-05 |
| 5,129 |
Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach |
2020 |
SIGMOD |
6.2582129e-05 |
| 5,454 |
High-Performance Row Pattern Recognition Using Joins |
2023 |
VLDB |
6.1225019e-05 |
| 5,630 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.056758e-05 |
| 5,835 |
Pre-training Summarization Models of Structured Datasets for Cardinality Estimation |
2022 |
VLDB |
5.9737602e-05 |
| 6,664 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.715134e-05 |
| 6,677 |
Index-Accelerated Pattern Matching in Event Stores |
2021 |
SIGMOD |
5.7105065e-05 |
| 6,714 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
5.6993812e-05 |
| 6,758 |
Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities |
2022 |
SIGMOD |
5.6877154e-05 |
| 6,824 |
ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation |
2024 |
SIGMOD |
5.670071e-05 |
| 7,334 |
Selectivity Functions of Range Queries are Learnable* |
2022 |
SIGMOD |
5.5473714e-05 |
| 7,344 |
Scalable Multi-Query Execution using Reinforcement Learning |
2021 |
SIGMOD |
5.5455974e-05 |
| 7,356 |
PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! |
2021 |
VLDB |
5.5421826e-05 |