| 1,608 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011169837 |
| 2,112 |
The Log-Structured Merge-Bush & the Wacky Continuum |
2019 |
SIGMOD |
9.5244583e-05 |
| 2,797 |
Chucky: A Succinct Cuckoo Filter for LSM-Tree |
2021 |
SIGMOD |
8.1116755e-05 |
| 3,466 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.0645718e-05 |
| 3,545 |
Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores |
2020 |
SIGMOD |
6.9831585e-05 |
| 3,797 |
Constructing and Analyzing the LSM Compaction Design Space |
2021 |
VLDB |
6.7552936e-05 |
| 3,970 |
Spooky: Granulating LSM-Tree Compactions Correctly |
2022 |
VLDB |
6.5756727e-05 |
| 4,227 |
Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine |
2022 |
VLDB |
6.3381409e-05 |
| 4,404 |
TreeLine: An Update-In-Place Key-Value Store for Modern Storage |
2023 |
VLDB |
6.2052645e-05 |
| 4,447 |
MorphoSys: Automatic Physical Design Metamorphosis for Distributed Database Systems |
2020 |
VLDB |
6.1745324e-05 |
| 4,448 |
Stable Learned Bloom Filters for Data Streams |
2020 |
VLDB |
6.1741316e-05 |
| 4,948 |
SplinterDB and Maplets: Improving the Tradeoffs in Key-Value Store Compaction Policy |
2023 |
SIGMOD |
5.810122e-05 |
| 5,289 |
LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning |
2022 |
SIGMOD |
5.5790771e-05 |
| 5,310 |
The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures |
2022 |
SIGMOD |
5.5713508e-05 |
| 5,313 |
Key-Value Storage Engines |
2020 |
SIGMOD |
5.5711707e-05 |
| 5,749 |
InfiniFilter: Expanding Filters to Infinity and Beyond |
2023 |
SIGMOD |
5.3420354e-05 |
| 5,787 |
Machine Learning for Databases |
2021 |
VLDB |
5.3256401e-05 |
| 5,801 |
Dissecting, Designing, and Optimizing LSM-based Data Stores |
2022 |
SIGMOD |
5.3217858e-05 |
| 6,298 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1182917e-05 |
| 6,394 |
Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty |
2022 |
VLDB |
5.0770427e-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 |
| 7,623 |
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads |
2023 |
SIGMOD |
4.6890662e-05 |
| 8,011 |
CAMAL: Optimizing LSM-trees via Active Learning |
2024 |
SIGMOD |
4.6022693e-05 |
| 8,333 |
How to Grow an LSM-tree? Towards Bridging the Gap Between Theory and Practice |
2025 |
SIGMOD |
4.5390511e-05 |
| 8,340 |
Deep Learning: Systems and Responsibility |
2021 |
SIGMOD |
4.5381546e-05 |
| 8,408 |
The next 50 Years in Database Indexing or: The Case for Automatically Generated Index Structures |
2022 |
VLDB |
4.5159669e-05 |
| 8,426 |
Time Series Representation for Visualization in Apache IoTDB |
2024 |
SIGMOD |
4.5098472e-05 |
| 8,524 |
Aleph Filter: To Infinity in Constant Time |
2024 |
VLDB |
4.4893996e-05 |
| 8,624 |
Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines |
2024 |
SIGMOD |
4.4786127e-05 |
| 8,804 |
ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads |
2026 |
VLDB |
4.4424232e-05 |
| 9,069 |
Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space |
2024 |
SIGMOD |
4.3983078e-05 |
| 9,322 |
Are Joins over LSM-trees Ready? Take RocksDB as an Example |
2025 |
VLDB |
4.351469e-05 |
| 9,370 |
FluidKV: Seamlessly Bridging the Gap between Indexing Performance and Memory-Footprint on Ultra-Fast Storage |
2024 |
VLDB |
4.3461752e-05 |
| 9,390 |
Rethinking The Compaction Policies in LSM-trees |
2025 |
SIGMOD |
4.341433e-05 |
| 9,529 |
Mnemosyne: Dynamic Workload-Aware BF Tuning via Accurate Statistics in LSM trees |
2025 |
SIGMOD |
4.3251912e-05 |
| 9,787 |
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format |
2024 |
SIGMOD |
4.2799988e-05 |
| 9,818 |
Generating Application-Specific Data Layouts for In-memory Databases |
2019 |
VLDB |
4.2733415e-05 |
| 9,891 |
DBMS Fitting: Why should we learn what we already know? |
2020 |
CIDR |
4.2573619e-05 |
| 10,182 |
Making LSM-Tree-based Key-Value Store Practical and Efficient for Multi-Tenant Serverless Cloud Databases |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,853 |
AXE: A Task Decomposition Approach to Learned LSM Tuning |
2025 |
VLDB |
4.1905499e-05 |
| 11,448 |
Learning Algorithms for Automatic Data Structure Design |
2021 |
SIGMOD |
4.1905499e-05 |
| 11,573 |
From Worst-Case to Average-Case Analysis: Accurate Latency Predictions for Key-Value Storage Engines |
2020 |
SIGMOD |
4.1905499e-05 |
| 11,591 |
Demonstration of Chestnut: An In-memory Data Layout Designer for Database Applications |
2020 |
SIGMOD |
4.1905499e-05 |