| 45 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.0004530684 |
| 157 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00028782395 |
| 204 |
Snorkel: Rapid Training Data Creation with Weak Supervision |
2018 |
VLDB |
0.00025523636 |
| 394 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00019400224 |
| 564 |
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics |
2020 |
VLDB |
0.0001653392 |
| 569 |
SageDB: A Learned Database System |
2019 |
CIDR |
0.00016472308 |
| 915 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.00013295102 |
| 1,028 |
MIRIS: Fast Object Track Queries in Video |
2020 |
SIGMOD |
0.00012632756 |
| 1,080 |
Snuba: Automating Weak Supervision to Label Training Data |
2019 |
VLDB |
0.0001239664 |
| 1,109 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00012227567 |
| 1,147 |
Cerebro: A Data System for Optimized Deep Learning Model Selection |
2020 |
VLDB |
0.00012057626 |
| 1,217 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00011709392 |
| 1,408 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.0001097193 |
| 1,416 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
0.00010940122 |
| 1,536 |
HELIX: Holistic Optimization for Accelerating Iterative Machine Learning |
2019 |
VLDB |
0.00010490973 |
| 1,629 |
The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models |
2018 |
SIGMOD |
0.0001024133 |
| 1,837 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
9.7435473e-05 |
| 1,925 |
Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn |
2019 |
CIDR |
9.5601154e-05 |
| 2,133 |
DBPal: A Fully Pluggable NL2SQL Training Pipeline |
2020 |
SIGMOD |
9.1819568e-05 |
| 2,189 |
An Intermediate Representation for Optimizing Machine Learning Pipelines |
2019 |
VLDB |
9.0524569e-05 |
| 2,595 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
8.4354632e-05 |
| 2,642 |
Accelerating Recommendation System Training by Leveraging Popular Choices |
2022 |
VLDB |
8.38146e-05 |
| 2,659 |
Panorama: A Data System for Unbounded Vocabulary Querying over Video |
2020 |
VLDB |
8.350357e-05 |
| 2,666 |
DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU |
2023 |
SIGMOD |
8.3415323e-05 |
| 3,122 |
End-to-end Optimization of Machine Learning Prediction Queries |
2022 |
SIGMOD |
7.7901348e-05 |
| 3,225 |
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics |
2021 |
VLDB |
7.6926736e-05 |
| 3,234 |
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition |
2021 |
VLDB |
7.6850504e-05 |
| 3,283 |
Towards Demystifying Serverless Machine Learning Training |
2021 |
SIGMOD |
7.6258432e-05 |
| 3,506 |
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines |
2022 |
SIGMOD |
7.4298791e-05 |
| 3,535 |
Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine |
2022 |
VLDB |
7.3934704e-05 |
| 3,553 |
Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe? |
2017 |
SIGMOD |
7.3775219e-05 |
| 3,633 |
Learning to Validate the Predictions of Black Box Classifiers on Unseen Data |
2020 |
SIGMOD |
7.3120989e-05 |
| 3,666 |
VSS: A Storage System for Video Analytics |
2021 |
SIGMOD |
7.2781557e-05 |
| 3,714 |
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers |
2019 |
VLDB |
7.24363e-05 |
| 3,825 |
The Case for a Learned Sorting Algorithm |
2020 |
SIGMOD |
7.1511577e-05 |
| 3,957 |
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning |
2021 |
SIGMOD |
7.0651521e-05 |
| 3,961 |
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches |
2021 |
VLDB |
7.0630139e-05 |
| 4,177 |
LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems |
2021 |
SIGMOD |
6.9157444e-05 |
| 4,240 |
A Method for Optimizing Opaque Filter Queries |
2020 |
SIGMOD |
6.8750034e-05 |
| 4,381 |
GOGGLES: Automatic Image Labeling with Affinity Coding |
2020 |
SIGMOD |
6.7988732e-05 |
| 4,509 |
FiGO: Fine-Grained Query Optimization in Video Analytics |
2022 |
SIGMOD |
6.7228506e-05 |
| 4,636 |
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning |
2022 |
SIGMOD |
6.656002e-05 |
| 4,819 |
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-Precision Learning |
2019 |
VLDB |
6.5610372e-05 |
| 4,899 |
Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce |
2021 |
SIGMOD |
6.5177988e-05 |
| 5,508 |
An Experimental Evaluation of Large Scale GBDT Systems |
2019 |
VLDB |
6.2581278e-05 |
| 5,858 |
Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra |
2021 |
SIGMOD |
6.1254857e-05 |
| 5,909 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
6.106668e-05 |
| 5,937 |
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access |
2022 |
SIGMOD |
6.0975341e-05 |
| 5,938 |
Towards instance-optimized data systems |
2021 |
VLDB |
6.097047e-05 |
| 5,999 |
Self-Organizing Data Containers |
2022 |
CIDR |
6.0775879e-05 |