| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 254 |
Snorkel: Rapid Training Data Creation with Weak Supervision |
2018 |
VLDB |
0.00030540555 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 683 |
Cerebro: A Data System for Optimized Deep Learning Model Selection |
2020 |
VLDB |
0.00018195476 |
| 696 |
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics |
2020 |
VLDB |
0.00018048935 |
| 801 |
SageDB: A Learned Database System |
2019 |
CIDR |
0.00016505496 |
| 1,160 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013586221 |
| 1,215 |
Snuba: Automating Weak Supervision to Label Training Data |
2019 |
VLDB |
0.0001323375 |
| 1,387 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012261568 |
| 1,388 |
MIRIS: Fast Object Track Queries in Video |
2020 |
SIGMOD |
0.00012260926 |
| 1,478 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.00011762542 |
| 1,666 |
HELIX: Holistic Optimization for Accelerating Iterative Machine Learning |
2019 |
VLDB |
0.0001096361 |
| 1,889 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010200865 |
| 2,115 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5257379e-05 |
| 2,157 |
The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models |
2018 |
SIGMOD |
9.416022e-05 |
| 2,321 |
DBPal: A Fully Pluggable NL2SQL Training Pipeline |
2020 |
SIGMOD |
9.03609e-05 |
| 2,350 |
An Intermediate Representation for Optimizing Machine Learning Pipelines |
2019 |
VLDB |
8.9788641e-05 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 2,422 |
DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU |
2023 |
SIGMOD |
8.8499665e-05 |
| 2,606 |
Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn |
2019 |
CIDR |
8.4645832e-05 |
| 2,688 |
Accelerating Recommendation System Training by Leveraging Popular Choices |
2022 |
VLDB |
8.2991144e-05 |
| 2,791 |
Towards Demystifying Serverless Machine Learning Training |
2021 |
SIGMOD |
8.1206618e-05 |
| 2,839 |
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition |
2021 |
VLDB |
8.0378978e-05 |
| 3,087 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5939896e-05 |
| 3,206 |
Panorama: A Data System for Unbounded Vocabulary Querying over Video |
2020 |
VLDB |
7.3826363e-05 |
| 3,293 |
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics |
2021 |
VLDB |
7.2629834e-05 |
| 3,363 |
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers |
2019 |
VLDB |
7.1731921e-05 |
| 3,407 |
End-to-end Optimization of Machine Learning Prediction Queries |
2022 |
SIGMOD |
7.1295646e-05 |
| 3,698 |
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines |
2022 |
SIGMOD |
6.8340435e-05 |
| 3,806 |
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning |
2021 |
SIGMOD |
6.7492837e-05 |
| 4,097 |
The Case for a Learned Sorting Algorithm |
2020 |
SIGMOD |
6.4551616e-05 |
| 4,110 |
Learning to Validate the Predictions of Black Box Classifiers on Unseen Data |
2020 |
SIGMOD |
6.4428544e-05 |
| 4,161 |
Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe? |
2017 |
SIGMOD |
6.3938006e-05 |
| 4,227 |
Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine |
2022 |
VLDB |
6.3434324e-05 |
| 4,269 |
VSS: A Storage System for Video Analytics |
2021 |
SIGMOD |
6.306798e-05 |
| 4,471 |
GOGGLES: Automatic Image Labeling with Affinity Coding |
2020 |
SIGMOD |
6.1555681e-05 |
| 4,557 |
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches |
2021 |
VLDB |
6.087611e-05 |
| 4,774 |
LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems |
2021 |
SIGMOD |
5.9316087e-05 |
| 4,909 |
A Method for Optimizing Opaque Filter Queries |
2020 |
SIGMOD |
5.8338804e-05 |
| 4,975 |
An Experimental Evaluation of Large Scale GBDT Systems |
2019 |
VLDB |
5.79026e-05 |
| 5,123 |
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-Precision Learning |
2019 |
VLDB |
5.6796998e-05 |
| 5,135 |
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning |
2022 |
SIGMOD |
5.6724721e-05 |
| 5,173 |
FiGO: Fine-Grained Query Optimization in Video Analytics |
2022 |
SIGMOD |
5.6447253e-05 |
| 5,333 |
Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce |
2021 |
SIGMOD |
5.5656575e-05 |
| 5,988 |
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access |
2022 |
SIGMOD |
5.2430981e-05 |
| 6,191 |
Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra |
2021 |
SIGMOD |
5.1642282e-05 |
| 6,279 |
Self-Organizing Data Containers |
2022 |
CIDR |
5.1295282e-05 |
| 6,297 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1227886e-05 |
| 6,456 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
5.0564619e-05 |