E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model
Summary: E2ETune frames DB knob tuning as seq2seq generation: fine-tunes a generative language model on synthetic workload→promising-configuration pairs produced by a novel data-generation pipeline. Produces one-shot, out-of-the-box config recommendations that match SOTA with far fewer workload replays. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xinmei Huang
- 2. Haoyang Li
- 3. Jing Zhang
- 4. Xinxin Zhao
- 5. Zhiming Yao
- 6. Yiyan Li
- 7. Tieying Zhang
- 8. Jianjun Chen
- 9. Hong Chen
- 10. Cuiping Li
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,047 | AgentTune: An Agent-Based Large Language Model Framework for Database Knob Tuning | 2026 | SIGMOD | 4.1905499e-05 |
| 10,093 | MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration | 2026 | SIGMOD | 4.1905499e-05 |
| 10,164 | ESTune: Bayesian Uncertainty-Guided Early Stopping for Database Configuration Tuning | 2026 | SIGMOD | 4.1905499e-05 |
| 10,212 | SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads | 2026 | SIGMOD | 4.1905499e-05 |
| 10,217 | This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! | 2026 | SIGMOD | 4.1905499e-05 |
| 10,278 | LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration | 2026 | VLDB | 4.1905499e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 35 of 35 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next