| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 281 |
LinkBench: a Database Benchmark Based on the Facebook Social Graph |
2013 |
SIGMOD |
0.0002906793 |
| 359 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.0002592783 |
| 379 |
bLSM: A General Purpose Log Structured Merge Tree |
2012 |
SIGMOD |
0.0002493527 |
| 424 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023616398 |
| 514 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.0002124895 |
| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 609 |
Monkey: Optimal Navigable Key-Value Store |
2017 |
SIGMOD |
0.0001923446 |
| 782 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016729063 |
| 806 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.00016434274 |
| 884 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015654004 |
| 1,169 |
SuRF: Practical Range Query Filtering with Fast Succinct Tries |
2018 |
SIGMOD |
0.00013536447 |
| 1,254 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.00013027411 |
| 1,311 |
Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging |
2018 |
SIGMOD |
0.00012657439 |
| 1,366 |
SlimDB: A Space-Efficient Key-Value Storage Engine For Semi-Sorted Data |
2017 |
VLDB |
0.00012357685 |
| 1,613 |
Realtime Data Processing at Facebook |
2016 |
SIGMOD |
0.00011140777 |
| 1,703 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010836769 |
| 1,827 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010390548 |
| 1,960 |
Compaction management in distributed key-value datastores |
2015 |
VLDB |
9.9521444e-05 |
| 2,004 |
X-Engine: An Optimized Storage Engine for Large-scale E-commerce Transaction Processing |
2019 |
SIGMOD |
9.811707e-05 |
| 2,109 |
The Log-Structured Merge-Bush & the Wacky Continuum |
2019 |
SIGMOD |
9.5318694e-05 |
| 2,596 |
WeTune: Automatic Discovery and Verification of Query Rewrite Rules |
2022 |
SIGMOD |
8.4729982e-05 |
| 2,606 |
Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn |
2019 |
CIDR |
8.4645832e-05 |
| 2,798 |
Chucky: A Succinct Cuckoo Filter for LSM-Tree |
2021 |
SIGMOD |
8.1080111e-05 |
| 3,142 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
7.4815444e-05 |
| 3,386 |
Lethe: A Tunable Delete-Aware LSM Engine |
2020 |
SIGMOD |
7.1577103e-05 |
| 3,544 |
Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores |
2020 |
SIGMOD |
6.9898874e-05 |
| 3,564 |
Accordion: Better Memory Organization for LSM Key-Value Stores |
2018 |
VLDB |
6.9669032e-05 |
| 3,793 |
Constructing and Analyzing the LSM Compaction Design Space |
2021 |
VLDB |
6.7617833e-05 |
| 3,965 |
Spooky: Granulating LSM-Tree Compactions Correctly |
2022 |
VLDB |
6.5820028e-05 |
| 4,227 |
Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine |
2022 |
VLDB |
6.3434324e-05 |
| 4,240 |
Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation |
2021 |
VLDB |
6.3318228e-05 |
| 4,588 |
Leaper: A Learned Prefetcher for Cache Invalidation in LSM-tree based Storage Engines |
2020 |
VLDB |
6.0655418e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 4,758 |
Optimization for Active Learning-based Interactive Database Exploration |
2019 |
VLDB |
5.9422515e-05 |
| 4,835 |
Proteus: A Self-Designing Range Filter |
2022 |
SIGMOD |
5.8905445e-05 |
| 5,356 |
LogKV: Exploiting Key-Value Stores for Event Log Processing |
2013 |
CIDR |
5.5509715e-05 |
| 5,535 |
Lightweight Cardinality Estimation in LSM-based Systems |
2018 |
SIGMOD |
5.4539235e-05 |
| 5,572 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
5.4277273e-05 |
| 5,791 |
Dissecting, Designing, and Optimizing LSM-based Data Stores |
2022 |
SIGMOD |
5.3268999e-05 |
| 5,918 |
Breaking Down Memory Walls: Adaptive Memory Management in LSM-based Storage Systems |
2021 |
VLDB |
5.2737135e-05 |
| 6,398 |
Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty |
2022 |
VLDB |
5.0819209e-05 |
| 7,620 |
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads |
2023 |
SIGMOD |
4.693568e-05 |
| 8,082 |
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems |
2022 |
SIGMOD |
4.5905454e-05 |
| 9,071 |
Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space |
2024 |
SIGMOD |
4.4025274e-05 |