| 3,655 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.8723042e-05 |
| 3,658 |
Towards a Hands-Free Query Optimizer through Deep Learning |
2019 |
CIDR |
6.8700949e-05 |
| 3,706 |
Every Row Counts: Combining Sketches and Sampling for Accurate Group-By Result Estimates |
2019 |
CIDR |
6.8232992e-05 |
| 3,719 |
To Partition, or Not to Partition, That is the Join Question in a Real System |
2021 |
SIGMOD |
6.8141176e-05 |
| 3,725 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
6.8117015e-05 |
| 3,729 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8078013e-05 |
| 3,781 |
A Learned Sketch for Subgraph Counting |
2021 |
SIGMOD |
6.7691344e-05 |
| 3,923 |
Pushing Data-Induced Predicates Through Joins in Big-Data Clusters |
2020 |
VLDB |
6.6232068e-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 |
| 4,017 |
Columnar Storage and List-based Processing for Graph Database Management Systems |
2021 |
VLDB |
6.5276062e-05 |
| 4,129 |
A Statistical Perspective on Discovering Functional Dependencies in Noisy Data |
2020 |
SIGMOD |
6.4208557e-05 |
| 4,157 |
Performance-Optimal Filtering: Bloom Overtakes Cuckoo at High Throughput |
2019 |
VLDB |
6.3935343e-05 |
| 4,171 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
6.3800823e-05 |
| 4,272 |
Looking Ahead Makes Query Plans Robust: Making the Initial Case with In-Memory Star Schema Data Warehouse Workloads |
2017 |
VLDB |
6.2933353e-05 |
| 4,352 |
Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning |
2021 |
VLDB |
6.2542257e-05 |
| 4,372 |
Sample Debiasing in the Themis Open World Database System |
2020 |
SIGMOD |
6.2367043e-05 |
| 4,382 |
HTAP Databases: What is New and What is Next |
2022 |
SIGMOD |
6.2318984e-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 |
| 4,464 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
6.1552798e-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 |
| 4,566 |
Adaptive Statistics in Oracle 12c |
2017 |
VLDB |
6.073045e-05 |
| 4,587 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0594195e-05 |
| 4,592 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.056004e-05 |
| 4,621 |
Automated Generation of Materialized Views in Oracle |
2020 |
VLDB |
6.036749e-05 |
| 4,658 |
PreQR: Pre-training Representation for SQL Understanding |
2022 |
SIGMOD |
6.0084453e-05 |
| 4,687 |
Deploying a Steered Query Optimizer in Production at Microsoft |
2022 |
SIGMOD |
5.9915268e-05 |
| 4,702 |
JSON Tiles: Fast Analytics on Semi-Structured Data |
2021 |
SIGMOD |
5.9796907e-05 |
| 4,730 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
5.9604983e-05 |
| 4,799 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.9082876e-05 |
| 4,996 |
Adaptive Work Placement for Query Processing on Heterogeneous Computing Resources |
2017 |
VLDB |
5.7697228e-05 |
| 5,001 |
GenRewrite: Query Rewriting via Large Language Models |
2026 |
SIGMOD |
5.7634197e-05 |
| 5,076 |
HyperBench: A Benchmark and Tool for Hypergraphs and Empirical Findings |
2019 |
PODS |
5.709895e-05 |
| 5,085 |
Guaranteeing the O~(AGM/OUT) Runtime for Uniform Sampling and Size Estimation over Joins |
2023 |
PODS |
5.7040225e-05 |
| 5,114 |
Columnstore and B+ tree – Are Hybrid Physical Designs Important? |
2018 |
SIGMOD |
5.6813929e-05 |
| 5,129 |
The Art of Balance: A RateupDBTM Experience of Building a CPU/GPU Hybrid Database Product |
2021 |
VLDB |
5.6724875e-05 |
| 5,316 |
Cuckoo Index: A Lightweight Secondary Index Structure |
2020 |
VLDB |
5.5688295e-05 |
| 5,339 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
5.5596755e-05 |
| 5,343 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5582234e-05 |
| 5,412 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
5.5200608e-05 |
| 5,506 |
Can Large Language Models Predict Data Correlations from Column Names? |
2023 |
VLDB |
5.4711611e-05 |
| 5,524 |
Facilitating SQL Query Composition and Analysis |
2020 |
SIGMOD |
5.4589341e-05 |
| 5,528 |
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts |
2022 |
SIGMOD |
5.4571136e-05 |
| 5,540 |
Permutable Compiled Queries: Dynamically Adapting Compiled Queries without Recompiling |
2021 |
VLDB |
5.450319e-05 |
| 5,543 |
Lightweight Cardinality Estimation in LSM-based Systems |
2018 |
SIGMOD |
5.4486922e-05 |
| 5,639 |
Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server |
2023 |
VLDB |
5.3972261e-05 |
| 5,645 |
Database Workload Characterization with Query Plan Encoders |
2022 |
VLDB |
5.3928148e-05 |
| 5,654 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
5.3882121e-05 |