Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation
Summary: Comprehensive evaluation of DB configuration tuning methods across modules, showing hyper-parameter optimization can boost tuning performance. Proposes an efficient surrogate-based unified benchmark to minimize evaluation cost and identify best algorithms per module. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xinyi Zhang (Alibaba; Peking University)
- 2. Zhuo Chang (Alibaba; Peking University)
- 3. Yang Li (Peking University)
- 4. Hong Wu (Alibaba)
- 5. Jian Tan (Alibaba)
- 6. Feifei Li (Alibaba)
- 7. Bin Cui (Peking University)
BibTeX Citation
@article{zhang_vldb22,
title = {{Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation}},
author = {Zhang, Xinyi and Chang, Zhuo and Li, Yang and Wu, Hong and Tan, Jian and Li, Feifei and Cui, Bin},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {9},
pages = {1808--1821},
doi = {10.14778/3538598.3538604},
url = {https://doi.org/10.14778/3538598.3538604},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 33 of 33 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next