| 938 |
Probase: A Probabilistic Taxonomy for Text Understanding |
2012 |
SIGMOD |
34 |
0.00012956092 |
| 1,261 |
Sampling-Based Query Re-Optimization |
2016 |
SIGMOD |
35 |
0.00011289724 |
| 1,282 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
59 |
0.00011197535 |
| 1,744 |
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions |
2021 |
VLDB |
22 |
9.7253583e-05 |
| 2,031 |
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads |
2018 |
VLDB |
16 |
9.1414653e-05 |
| 2,219 |
Magpie: Python at Speed and Scale using Cloud Backends |
2021 |
CIDR |
18 |
8.8186113e-05 |
| 3,256 |
Towards Demystifying Serverless Machine Learning Training |
2021 |
SIGMOD |
9 |
7.4732677e-05 |
| 3,340 |
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition |
2021 |
VLDB |
11 |
7.3886578e-05 |
| 3,384 |
Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads |
2013 |
VLDB |
23 |
7.3392425e-05 |
| 3,520 |
MLog: Towards Declarative In-Database Machine Learning |
2017 |
VLDB |
12 |
7.2180592e-05 |
| 4,990 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
21 |
6.3120056e-05 |
| 5,645 |
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning |
2022 |
SIGMOD |
16 |
6.0354456e-05 |
| 5,670 |
Uncertainty Aware Query Execution Time Prediction |
2014 |
VLDB |
14 |
6.0266818e-05 |
| 6,189 |
Helios: Hyperscale Indexing for the Cloud & Edge |
2020 |
VLDB |
5 |
5.8391363e-05 |
| 6,474 |
In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle |
2022 |
SIGMOD |
5 |
5.7555547e-05 |
| 7,094 |
Plan Stitch: Harnessing the Best of Many Plans |
2018 |
VLDB |
10 |
5.5863735e-05 |
| 7,765 |
Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization |
2019 |
VLDB |
4 |
5.4400458e-05 |
| 7,911 |
MLBench: Benchmarking Machine Learning Services Against Human Experts |
2018 |
VLDB |
3 |
5.4124754e-05 |
| 7,921 |
DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning |
2022 |
VLDB |
12 |
5.4098206e-05 |
| 8,891 |
Optimization of Threshold Functions over Streams |
2021 |
VLDB |
1 |
5.2423828e-05 |
| 9,117 |
Hyperspace: The Indexing Subsystem of Azure Synapse |
2021 |
VLDB |
1 |
5.208561e-05 |
| 9,474 |
Ease.ml in Action: Towards Multi-tenant Declarative Learning Services |
2018 |
VLDB |
4 |
5.1581396e-05 |
| 9,829 |
Wii: Dynamic Budget Reallocation In Index Tuning |
2024 |
SIGMOD |
7 |
5.1066866e-05 |
| 9,983 |
Wred: Workload Reduction for Scalable Index Tuning |
2024 |
SIGMOD |
6 |
5.0860177e-05 |
| 10,592 |
Understanding the Impact of Data Noise in Federated Learning: [Experiments & Analysis] |
2026 |
SIGMOD |
0 |
4.9605343e-05 |
| 10,676 |
Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] |
2026 |
SIGMOD |
1 |
4.9605343e-05 |
| 10,753 |
RIB: Robust Learning-based Index Benefit Estimation |
2026 |
SIGMOD |
0 |
4.9605343e-05 |
| 10,982 |
Tuning the Lookahead Distance for PostgreSQL Asynchronous IO |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 10,988 |
Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server |
2026 |
VLDB |
0 |
4.9605343e-05 |
| 11,288 |
Esc: An Early-Stopping Checker for Budget-aware Index Tuning |
2025 |
VLDB |
4 |
4.9605343e-05 |
| 11,986 |
Ease.ML: A Lifecycle Management System for MLDev and MLOps |
2021 |
CIDR |
0 |
4.9605343e-05 |
| 12,154 |
Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development |
2020 |
VLDB |
1 |
4.9605343e-05 |
| 12,850 |
Search Your Memory ! - An Associative Memory Based Desktop Search System |
2009 |
SIGMOD |
0 |
4.9605343e-05 |