| 935 |
Probase: A Probabilistic Taxonomy for Text Understanding |
2012 |
SIGMOD |
34 |
0.00012998175 |
| 1,258 |
Sampling-Based Query Re-Optimization |
2016 |
SIGMOD |
35 |
0.00011308863 |
| 1,280 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
59 |
0.00011224914 |
| 1,848 |
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions |
2021 |
VLDB |
21 |
9.5075544e-05 |
| 2,040 |
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads |
2018 |
VLDB |
16 |
9.1477132e-05 |
| 2,208 |
Magpie: Python at Speed and Scale using Cloud Backends |
2021 |
CIDR |
18 |
8.8445332e-05 |
| 3,242 |
Towards Demystifying Serverless Machine Learning Training |
2021 |
SIGMOD |
9 |
7.4967268e-05 |
| 3,332 |
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition |
2021 |
VLDB |
11 |
7.4130985e-05 |
| 3,519 |
Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads |
2013 |
VLDB |
22 |
7.2361015e-05 |
| 3,524 |
MLog: Towards Declarative In-Database Machine Learning |
2017 |
VLDB |
12 |
7.2303094e-05 |
| 4,970 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
21 |
6.3319052e-05 |
| 5,632 |
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning |
2022 |
SIGMOD |
16 |
6.0554368e-05 |
| 5,658 |
Uncertainty Aware Query Execution Time Prediction |
2014 |
VLDB |
14 |
6.0460211e-05 |
| 6,176 |
Helios: Hyperscale Indexing for the Cloud & Edge |
2020 |
VLDB |
5 |
5.8585082e-05 |
| 6,460 |
In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle |
2022 |
SIGMOD |
5 |
5.7746493e-05 |
| 7,074 |
Plan Stitch: Harnessing the Best of Many Plans |
2018 |
VLDB |
10 |
5.604902e-05 |
| 7,751 |
Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization |
2019 |
VLDB |
4 |
5.4580922e-05 |
| 7,894 |
MLBench: Benchmarking Machine Learning Services Against Human Experts |
2018 |
VLDB |
3 |
5.4304318e-05 |
| 7,904 |
DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning |
2022 |
VLDB |
12 |
5.4277674e-05 |
| 8,873 |
Optimization of Threshold Functions over Streams |
2021 |
VLDB |
1 |
5.2597749e-05 |
| 9,098 |
Hyperspace: The Indexing Subsystem of Azure Synapse |
2021 |
VLDB |
1 |
5.2258409e-05 |
| 9,447 |
Ease.ml in Action: Towards Multi-tenant Declarative Learning Services |
2018 |
VLDB |
4 |
5.1752522e-05 |
| 9,796 |
Wii: Dynamic Budget Reallocation In Index Tuning |
2024 |
SIGMOD |
7 |
5.1236285e-05 |
| 9,950 |
Wred: Workload Reduction for Scalable Index Tuning |
2024 |
SIGMOD |
6 |
5.102891e-05 |
| 10,526 |
Understanding the Impact of Data Noise in Federated Learning: [Experiments & Analysis] |
2026 |
SIGMOD |
0 |
4.9769913e-05 |
| 10,615 |
Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] |
2026 |
SIGMOD |
1 |
4.9769913e-05 |
| 10,692 |
RIB: Robust Learning-based Index Benefit Estimation |
2026 |
SIGMOD |
0 |
4.9769913e-05 |
| 10,921 |
Tuning the Lookahead Distance for PostgreSQL Asynchronous IO |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 10,927 |
Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 11,232 |
Esc: An Early-Stopping Checker for Budget-aware Index Tuning |
2025 |
VLDB |
4 |
4.9769913e-05 |
| 11,942 |
Ease.ML: A Lifecycle Management System for MLDev and MLOps |
2021 |
CIDR |
0 |
4.9769913e-05 |
| 12,110 |
Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development |
2020 |
VLDB |
1 |
4.9769913e-05 |
| 12,807 |
Search Your Memory ! - An Associative Memory Based Desktop Search System |
2009 |
SIGMOD |
0 |
4.9769913e-05 |