| 85 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00035864347 |
| 145 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.0002908188 |
| 318 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00021167555 |
| 362 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019989474 |
| 373 |
Umbra: A Disk-Based System with In-Memory Performance |
2020 |
CIDR |
0.00019711632 |
| 406 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.00019045544 |
| 461 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.00017829982 |
| 512 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00017050173 |
| 688 |
Cardinality Estimation Done Right: Index-Based Join Sampling |
2017 |
CIDR |
0.00014753664 |
| 712 |
Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins |
2019 |
VLDB |
0.00014578373 |
| 784 |
VerdictDB: Universalizing Approximate Query Processing |
2018 |
SIGMOD |
0.00014012614 |
| 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 |
| 1,186 |
Adaptive Optimization of Very Large Join Queries |
2018 |
SIGMOD |
0.0001160797 |
| 1,199 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
0.00011563985 |
| 1,279 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00011226878 |
| 1,397 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
0.00010789242 |
| 1,433 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
0.00010677711 |
| 1,465 |
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities |
2019 |
SIGMOD |
0.00010576304 |
| 1,488 |
Efficient Discovery of Approximate Dependencies |
2018 |
VLDB |
0.00010517437 |
| 1,515 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
0.00010417728 |
| 1,516 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
0.00010402594 |
| 1,542 |
Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses |
2018 |
VLDB |
0.00010308631 |
| 1,580 |
Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries |
2020 |
SIGMOD |
0.00010180835 |
| 1,596 |
Adopting Worst-Case Optimal Joins in Relational Database Systems |
2020 |
VLDB |
0.00010127607 |
| 1,603 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2019 |
SIGMOD |
0.00010097649 |
| 1,734 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
9.7545773e-05 |
| 1,786 |
Procedural Extensions of SQL: Understanding their usage in the wild |
2021 |
VLDB |
9.6326265e-05 |
| 1,800 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2018 |
VLDB |
9.6093317e-05 |
| 1,857 |
Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases |
2020 |
VLDB |
9.4929201e-05 |
| 1,952 |
Quantifying TPC-H Choke Points and Their Optimizations |
2020 |
VLDB |
9.3189525e-05 |
| 1,975 |
CodexDB: Synthesizing Code for Query Processing from Natural Language Instructions using GPT-3 Codex |
2022 |
VLDB |
9.2807031e-05 |
| 2,004 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
9.2065719e-05 |
| 2,039 |
LLM-R^2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency |
2025 |
VLDB |
9.1493268e-05 |
| 2,210 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.8257742e-05 |
| 2,216 |
Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models |
2017 |
VLDB |
8.8177753e-05 |
| 2,217 |
Computing the Shapley Value of Facts in Query Answering |
2022 |
SIGMOD |
8.8172185e-05 |
| 2,250 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
8.7533306e-05 |
| 2,314 |
BtrBlocks: Efficient Columnar Compression for Data Lakes |
2023 |
SIGMOD |
8.6533171e-05 |
| 2,342 |
Learned Cardinality Estimation: An In-depth Study |
2022 |
SIGMOD |
8.6060437e-05 |
| 2,395 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
8.5281914e-05 |
| 2,478 |
Learning a Partitioning Advisor for Cloud Databases |
2020 |
SIGMOD |
8.4079121e-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 |
| 2,634 |
Neural Subgraph Counting with Wasserstein Estimator |
2022 |
SIGMOD |
8.1993804e-05 |
| 2,690 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
8.1258173e-05 |
| 2,772 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.035288e-05 |
| 2,818 |
To Partition, or Not to Partition, That is the Join Question in a Real System |
2021 |
SIGMOD |
7.9739791e-05 |
| 2,824 |
G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching |
2020 |
SIGMOD |
7.9698957e-05 |
| 2,834 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
7.9560627e-05 |