| 1,611 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011147324 |
| 2,109 |
The Log-Structured Merge-Bush & the Wacky Continuum |
2019 |
SIGMOD |
9.5318694e-05 |
| 2,798 |
Chucky: A Succinct Cuckoo Filter for LSM-Tree |
2021 |
SIGMOD |
8.1080111e-05 |
| 3,473 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.062864e-05 |
| 3,544 |
Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores |
2020 |
SIGMOD |
6.9898874e-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 |
| 4,427 |
TreeLine: An Update-In-Place Key-Value Store for Modern Storage |
2023 |
VLDB |
6.1965873e-05 |
| 4,446 |
Stable Learned Bloom Filters for Data Streams |
2020 |
VLDB |
6.1800659e-05 |
| 4,453 |
MorphoSys: Automatic Physical Design Metamorphosis for Distributed Database Systems |
2020 |
VLDB |
6.1723632e-05 |
| 4,945 |
SplinterDB and Maplets: Improving the Tradeoffs in Key-Value Store Compaction Policy |
2023 |
SIGMOD |
5.8157107e-05 |
| 5,308 |
Key-Value Storage Engines |
2020 |
SIGMOD |
5.576303e-05 |
| 5,371 |
LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning |
2022 |
SIGMOD |
5.5428776e-05 |
| 5,428 |
The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures |
2022 |
SIGMOD |
5.5091613e-05 |
| 5,739 |
InfiniFilter: Expanding Filters to Infinity and Beyond |
2023 |
SIGMOD |
5.3471718e-05 |
| 5,791 |
Dissecting, Designing, and Optimizing LSM-based Data Stores |
2022 |
SIGMOD |
5.3268999e-05 |
| 5,861 |
Machine Learning for Databases |
2021 |
VLDB |
5.298883e-05 |
| 6,297 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1227886e-05 |
| 6,398 |
Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty |
2022 |
VLDB |
5.0819209e-05 |
| 6,456 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
5.0564619e-05 |
| 7,343 |
LSM-Trees and B-Trees: The Best of Both Worlds |
2019 |
SIGMOD |
4.7568442e-05 |
| 7,620 |
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads |
2023 |
SIGMOD |
4.693568e-05 |
| 8,009 |
CAMAL: Optimizing LSM-trees via Active Learning |
2024 |
SIGMOD |
4.6066863e-05 |
| 8,339 |
How to Grow an LSM-tree? Towards Bridging the Gap Between Theory and Practice |
2025 |
SIGMOD |
4.5434069e-05 |
| 8,346 |
Deep Learning: Systems and Responsibility |
2021 |
SIGMOD |
4.5420668e-05 |
| 8,414 |
The next 50 Years in Database Indexing or: The Case for Automatically Generated Index Structures |
2022 |
VLDB |
4.5203005e-05 |
| 8,434 |
Time Series Representation for Visualization in Apache IoTDB |
2024 |
SIGMOD |
4.5141748e-05 |
| 8,525 |
Aleph Filter: To Infinity in Constant Time |
2024 |
VLDB |
4.4937074e-05 |
| 8,627 |
Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines |
2024 |
SIGMOD |
4.4829101e-05 |
| 8,805 |
ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads |
2026 |
VLDB |
4.4466855e-05 |
| 9,071 |
Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space |
2024 |
SIGMOD |
4.4025274e-05 |
| 9,317 |
Are Joins over LSM-trees Ready? Take RocksDB as an Example |
2025 |
VLDB |
4.3556432e-05 |
| 9,362 |
FluidKV: Seamlessly Bridging the Gap between Indexing Performance and Memory-Footprint on Ultra-Fast Storage |
2024 |
VLDB |
4.3503444e-05 |
| 9,386 |
Rethinking The Compaction Policies in LSM-trees |
2025 |
SIGMOD |
4.3455975e-05 |
| 9,529 |
Mnemosyne: Dynamic Workload-Aware BF Tuning via Accurate Statistics in LSM trees |
2025 |
SIGMOD |
4.32934e-05 |
| 9,806 |
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format |
2024 |
SIGMOD |
4.2805224e-05 |
| 9,819 |
Generating Application-Specific Data Layouts for In-memory Databases |
2019 |
VLDB |
4.2774401e-05 |
| 9,892 |
DBMS Fitting: Why should we learn what we already know? |
2020 |
CIDR |
4.261445e-05 |
| 10,182 |
Making LSM-Tree-based Key-Value Store Practical and Efficient for Multi-Tenant Serverless Cloud Databases |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,849 |
AXE: A Task Decomposition Approach to Learned LSM Tuning |
2025 |
VLDB |
4.1945683e-05 |
| 11,445 |
Learning Algorithms for Automatic Data Structure Design |
2021 |
SIGMOD |
4.1945683e-05 |
| 11,569 |
From Worst-Case to Average-Case Analysis: Accurate Latency Predictions for Key-Value Storage Engines |
2020 |
SIGMOD |
4.1945683e-05 |
| 11,587 |
Demonstration of Chestnut: An In-memory Data Layout Designer for Database Applications |
2020 |
SIGMOD |
4.1945683e-05 |