| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 252 |
Snorkel: Rapid Training Data Creation with Weak Supervision |
2018 |
VLDB |
0.00030532082 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 684 |
Cerebro: A Data System for Optimized Deep Learning Model Selection |
2020 |
VLDB |
0.00018152321 |
| 694 |
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics |
2020 |
VLDB |
0.00018031141 |
| 796 |
SageDB: A Learned Database System |
2019 |
CIDR |
0.00016541749 |
| 1,162 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013573136 |
| 1,218 |
Snuba: Automating Weak Supervision to Label Training Data |
2019 |
VLDB |
0.00013221309 |
| 1,388 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012249747 |
| 1,390 |
MIRIS: Fast Object Track Queries in Video |
2020 |
SIGMOD |
0.00012242018 |
| 1,464 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.0001184772 |
| 1,666 |
HELIX: Holistic Optimization for Accelerating Iterative Machine Learning |
2019 |
VLDB |
0.00010955907 |
| 1,887 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010201938 |
| 2,108 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5283642e-05 |
| 2,153 |
The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models |
2018 |
SIGMOD |
9.418541e-05 |
| 2,325 |
DBPal: A Fully Pluggable NL2SQL Training Pipeline |
2020 |
SIGMOD |
9.0277894e-05 |
| 2,355 |
An Intermediate Representation for Optimizing Machine Learning Pipelines |
2019 |
VLDB |
8.9727612e-05 |
| 2,399 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8869693e-05 |
| 2,425 |
DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU |
2023 |
SIGMOD |
8.8414587e-05 |
| 2,606 |
Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn |
2019 |
CIDR |
8.4621503e-05 |
| 2,690 |
Accelerating Recommendation System Training by Leveraging Popular Choices |
2022 |
VLDB |
8.2911466e-05 |
| 2,795 |
Towards Demystifying Serverless Machine Learning Training |
2021 |
SIGMOD |
8.1135606e-05 |
| 2,845 |
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition |
2021 |
VLDB |
8.0301674e-05 |
| 3,092 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5869574e-05 |
| 3,212 |
Panorama: A Data System for Unbounded Vocabulary Querying over Video |
2020 |
VLDB |
7.3772955e-05 |
| 3,291 |
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics |
2021 |
VLDB |
7.2607192e-05 |
| 3,372 |
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers |
2019 |
VLDB |
7.1633912e-05 |
| 3,409 |
End-to-end Optimization of Machine Learning Prediction Queries |
2022 |
SIGMOD |
7.1240791e-05 |
| 3,694 |
Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines |
2022 |
SIGMOD |
6.8316905e-05 |
| 3,807 |
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning |
2021 |
SIGMOD |
6.7428898e-05 |
| 4,086 |
The Case for a Learned Sorting Algorithm |
2020 |
SIGMOD |
6.4579358e-05 |
| 4,113 |
Learning to Validate the Predictions of Black Box Classifiers on Unseen Data |
2020 |
SIGMOD |
6.4326771e-05 |
| 4,160 |
Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe? |
2017 |
SIGMOD |
6.3886736e-05 |
| 4,227 |
Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine |
2022 |
VLDB |
6.3381409e-05 |
| 4,265 |
VSS: A Storage System for Video Analytics |
2021 |
SIGMOD |
6.3012892e-05 |
| 4,471 |
GOGGLES: Automatic Image Labeling with Affinity Coding |
2020 |
SIGMOD |
6.1496765e-05 |
| 4,601 |
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches |
2021 |
VLDB |
6.05274e-05 |
| 4,779 |
LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems |
2021 |
SIGMOD |
5.9259373e-05 |
| 4,909 |
A Method for Optimizing Opaque Filter Queries |
2020 |
SIGMOD |
5.8354189e-05 |
| 4,924 |
An Experimental Evaluation of Large Scale GBDT Systems |
2019 |
VLDB |
5.8211961e-05 |
| 5,124 |
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-Precision Learning |
2019 |
VLDB |
5.6747536e-05 |
| 5,168 |
FiGO: Fine-Grained Query Optimization in Video Analytics |
2022 |
SIGMOD |
5.6446115e-05 |
| 5,232 |
Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning |
2022 |
SIGMOD |
5.6094155e-05 |
| 5,332 |
Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce |
2021 |
SIGMOD |
5.5640779e-05 |
| 5,992 |
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access |
2022 |
SIGMOD |
5.2380905e-05 |
| 6,194 |
Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra |
2021 |
SIGMOD |
5.1592984e-05 |
| 6,237 |
Self-Organizing Data Containers |
2022 |
CIDR |
5.1371094e-05 |
| 6,298 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1182917e-05 |
| 6,440 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
5.0546781e-05 |