| 1,347 |
X-Engine: An Optimized Storage Engine for Large-scale E-commerce Transaction Processing |
2019 |
SIGMOD |
0.00011073571 |
| 1,502 |
The Log-Structured Merge-Bush & the Wacky Continuum |
2019 |
SIGMOD |
0.00010557694 |
| 1,942 |
Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn |
2019 |
CIDR |
9.4451535e-05 |
| 1,954 |
Chucky: A Succinct Cuckoo Filter for LSM-Tree |
2021 |
SIGMOD |
9.4208165e-05 |
| 2,698 |
Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores |
2020 |
SIGMOD |
8.2450522e-05 |
| 2,755 |
Lethe: A Tunable Delete-Aware LSM Engine |
2020 |
SIGMOD |
8.163097e-05 |
| 3,005 |
Constructing and Analyzing the LSM Compaction Design Space |
2021 |
VLDB |
7.8608206e-05 |
| 3,054 |
Spooky: Granulating LSM-Tree Compactions Correctly |
2022 |
VLDB |
7.8090808e-05 |
| 3,587 |
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements |
2022 |
SIGMOD |
7.2829345e-05 |
| 3,758 |
Nova-LSM: A Distributed, Component-based LSM-tree Key-value Store |
2021 |
SIGMOD |
7.1471346e-05 |
| 3,773 |
SplinterDB and Maplets: Improving the Tradeoffs in Key-Value Store Compaction Policy |
2023 |
SIGMOD |
7.1384476e-05 |
| 4,262 |
On Performance Stability in LSM-based Storage Systems |
2020 |
VLDB |
6.7965035e-05 |
| 4,664 |
GRF: A Global Range Filter for LSM-Trees with Shape Encoding |
2024 |
SIGMOD |
6.5787544e-05 |
| 4,960 |
InfiniFilter: Expanding Filters to Infinity and Beyond |
2023 |
SIGMOD |
6.4280133e-05 |
| 4,993 |
Key-Value Storage Engines |
2020 |
SIGMOD |
6.4096682e-05 |
| 5,226 |
Breaking Down Memory Walls: Adaptive Memory Management in LSM-based Storage Systems |
2021 |
VLDB |
6.3090383e-05 |
| 5,229 |
Dissecting, Designing, and Optimizing LSM-based Data Stores |
2022 |
SIGMOD |
6.307514e-05 |
| 5,692 |
Spatial Independent Range Sampling |
2021 |
SIGMOD |
6.119196e-05 |
| 5,766 |
Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty |
2022 |
VLDB |
6.094771e-05 |
| 5,917 |
Near-Data Processing in Database Systems on Native Computational Storage under HTAP Workloads |
2022 |
VLDB |
6.043185e-05 |
| 5,978 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
6.0212877e-05 |
| 6,139 |
Toward a Better Understanding and Evaluation of Tree Structures on Flash SSDs |
2021 |
VLDB |
5.9638351e-05 |
| 6,456 |
LSMGraph: A High-Performance Dynamic Graph Storage System with Multi-Level CSR |
2024 |
SIGMOD |
5.8741786e-05 |
| 6,539 |
Prefix Filter: Practically and Theoretically Better Than Bloom |
2022 |
VLDB |
5.8473945e-05 |
| 6,679 |
Revisiting the Design of LSM-tree Based OLTP Storage Engine with Persistent Memory |
2021 |
VLDB |
5.8050516e-05 |
| 6,843 |
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads |
2023 |
SIGMOD |
5.7573899e-05 |
| 7,208 |
TimeUnion: An Efficient Architecture with Unified Data Model for Timeseries Management Systems on Hybrid Cloud Storage |
2022 |
SIGMOD |
5.6727339e-05 |
| 7,274 |
LSM-Trees and B-Trees: The Best of Both Worlds |
2019 |
SIGMOD |
5.658769e-05 |
| 7,459 |
Efficient Data Ingestion and Query Processing for LSM-Based Storage Systems |
2019 |
VLDB |
5.6119467e-05 |
| 7,693 |
CaaS-LSM: Compaction-as-a-Service for LSM-based Key-Value Stores in Storage Disaggregated Infrastructure |
2024 |
SIGMOD |
5.5663461e-05 |
| 7,757 |
CAMAL: Optimizing LSM-trees via Active Learning |
2024 |
SIGMOD |
5.5508469e-05 |
| 7,776 |
Aleph Filter: To Infinity in Constant Time |
2024 |
VLDB |
5.5464036e-05 |
| 8,156 |
How to Grow an LSM-tree? Towards Bridging the Gap Between Theory and Practice |
2025 |
SIGMOD |
5.4765648e-05 |
| 8,360 |
SA-LSM: Optimize Data Layout for LSM-tree Based Storage using Survival Analysis |
2022 |
VLDB |
5.4444442e-05 |
| 8,409 |
Time Series Representation for Visualization in Apache IoTDB |
2024 |
SIGMOD |
5.4311904e-05 |
| 8,463 |
Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines |
2024 |
SIGMOD |
5.4205593e-05 |
| 8,475 |
Mnemosyne: Dynamic Workload-Aware BF Tuning via Accurate Statistics in LSM trees |
2025 |
SIGMOD |
5.4171036e-05 |
| 8,591 |
A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach |
2025 |
SIGMOD |
5.4059856e-05 |
| 8,790 |
Practical Dynamic Extension for Sampling Indexes |
2023 |
SIGMOD |
5.3722049e-05 |
| 8,799 |
FishStore: Faster Ingestion with Subset Hashing |
2019 |
SIGMOD |
5.369237e-05 |
| 8,811 |
ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads |
2026 |
VLDB |
5.3652966e-05 |
| 8,880 |
Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity |
2022 |
SIGMOD |
5.3524255e-05 |
| 8,896 |
Aster: Enhancing LSM-structures for Scalable Graph Database |
2025 |
SIGMOD |
5.3495662e-05 |
| 8,897 |
BACH: Bridging Adjacency List and CSR Format using LSM-Trees for HGTAP Workloads |
2025 |
VLDB |
5.3495662e-05 |
| 8,919 |
MirrorKV: An Efficient Key-Value Store on Hybrid Cloud Storage with Balanced Performance of Compaction and Querying |
2023 |
SIGMOD |
5.3483178e-05 |
| 9,021 |
Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space |
2024 |
SIGMOD |
5.3305499e-05 |
| 9,457 |
Rethinking The Compaction Policies in LSM-trees |
2025 |
SIGMOD |
5.2642945e-05 |
| 9,465 |
Are Joins over LSM-trees Ready? Take RocksDB as an Example |
2025 |
VLDB |
5.2634238e-05 |
| 9,481 |
The End of Moore’s Law and the Rise of The Data Processor |
2021 |
VLDB |
5.2634238e-05 |
| 9,506 |
FluidKV: Seamlessly Bridging the Gap between Indexing Performance and Memory-Footprint on Ultra-Fast Storage |
2024 |
VLDB |
5.258497e-05 |