| 1 |
Access Path Selection in a Relational Database Management System |
1979 |
SIGMOD |
0.0040465394 |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036859633 |
| 281 |
LinkBench: a Database Benchmark Based on the Facebook Social Graph |
2013 |
SIGMOD |
0.00029084275 |
| 371 |
Self-Driving Database Management Systems |
2017 |
CIDR |
0.00025382677 |
| 407 |
Database Cracking |
2007 |
CIDR |
0.00023941779 |
| 454 |
An Overview of Query Optimization in Relational Systems |
1998 |
PODS |
0.00022796106 |
| 608 |
Monkey: Optimal Navigable Key-Value Store |
2017 |
SIGMOD |
0.00019233548 |
| 796 |
SageDB: A Learned Database System |
2019 |
CIDR |
0.00016541749 |
| 892 |
Faster: A Concurrent Key-Value Store with In-Place Updates |
2018 |
SIGMOD |
0.00015522869 |
| 2,050 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
9.6883066e-05 |
| 2,128 |
SQLGraph: An Efficient Relational-Based Property Graph Store |
2015 |
SIGMOD |
9.4804485e-05 |
| 2,153 |
The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models |
2018 |
SIGMOD |
9.418541e-05 |
| 2,606 |
Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn |
2019 |
CIDR |
8.4621503e-05 |
| 2,977 |
ForkBase: An Efficient Storage Engine for Blockchain and Forkable Applications |
2018 |
VLDB |
7.7850958e-05 |
| 3,363 |
Lethe: A Tunable Delete-Aware LSM Engine |
2020 |
SIGMOD |
7.1680649e-05 |
| 3,545 |
Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores |
2020 |
SIGMOD |
6.9831585e-05 |
| 3,645 |
Autoscaling Tiered Cloud Storage in Anna |
2019 |
VLDB |
6.882432e-05 |
| 3,692 |
iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases |
2019 |
VLDB |
6.8328808e-05 |
| 3,753 |
Choosing A Cloud DBMS: Architectures and Tradeoffs |
2019 |
VLDB |
6.7850001e-05 |
| 3,797 |
Constructing and Analyzing the LSM Compaction Design Space |
2021 |
VLDB |
6.7552936e-05 |
| 4,160 |
Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe? |
2017 |
SIGMOD |
6.3886736e-05 |
| 4,659 |
Nova-LSM: A Distributed, Component-based LSM-tree Key-value Store |
2021 |
SIGMOD |
6.0076537e-05 |
| 5,313 |
Key-Value Storage Engines |
2020 |
SIGMOD |
5.5711707e-05 |
| 5,367 |
LogKV: Exploiting Key-Value Stores for Event Log Processing |
2013 |
CIDR |
5.5461097e-05 |
| 5,847 |
Order-Preserving Key Compression for In-Memory Search Trees |
2020 |
SIGMOD |
5.3040014e-05 |
| 6,440 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
5.0546781e-05 |
| 7,341 |
LSM-Trees and B-Trees: The Best of Both Worlds |
2019 |
SIGMOD |
4.7522998e-05 |
| 7,999 |
nKV in Action: Accelerating KV-Stores on Native Computational Storage with Near-Data Processing |
2020 |
VLDB |
4.6065616e-05 |