Managing the configurations of a database system poses significant challenges
due to the multitude of configuration knobs that impact various system
aspects.The lack of standardization, independence, and universality among these
knobs further complicates the task of determining the optimal settings.To
address this issue, an automated solution leveraging supervised and
unsupervised machine learning techniques was developed.This solution aims to
identify influential knobs, analyze previously unseen workloads, and provide
recommendations for knob settings.The effectiveness of this approach is
demonstrated through the evaluation of a new tool called OtterTune [1] on three
different database management systems (DBMSs).The results indicate that
OtterTune's recommendations are comparable to or even surpass the
configurations generated by existing tools or human experts.In this study, we
build upon the automated technique introduced in the original OtterTune paper,
utilizing previously collected training data to optimize new DBMS
deployments.By employing supervised and unsupervised machine learning methods,
we focus on improving latency prediction.Our approach expands upon the methods
proposed in the original paper by incorporating GMM clustering to streamline
metrics selection and combining ensemble models (such as RandomForest) with
non-linear models (like neural networks) for more accurate prediction modeling.

通过使用 OtterTune 自动化工具，利用监督和无监督机器学习技术实现对数据库系统配置的自动管理，并通过对比实验证明，其生成的配置与人工专家配置相当或甚至更优，本文则是在原有的自动化技术基础上，利用机器学习方法进一步优化了新的数据库系统的部署，特别是在准确性预估上。

使用深度学习进行数据库管理系统的自动调优

Utilizing deep learning for automated tuning of database management  systems

Online users are constantly seeking experiences, such as a hotel with clean
rooms and a lively bar, or a restaurant for a romantic rendezvous. However,
e-commerce search engines only support queries involving objective attributes
such as location, price, and cuisine, and any experiential data is relegated to
text reviews.
In order to support experiential queries, a database system needs to model
subjective data and also be able to process queries where the user can express
varied subjective experiences in words chosen by the user, in addition to
specifying predicates involving objective attributes. This paper introduces
Opine, a subjective database system that addresses these challenges. We
introduce a data model for subjective databases. We describe how Opine
translates subjective queries against the subjective database schema, which is
done by matching the user query phrases to the underlying schema. We also show
how the experiential conditions specified by the user can be combined and the
results aggregated and ranked. We demonstrate that subjective databases satisfy
user needs more effectively and accurately than alternative techniques through
experiments with real data of hotel and restaurant reviews.

本论文介绍了 Opine，这是一个主观数据库系统，它通过建立主观数据库模型和用户查询短语匹配，支持用户主观体验查询，并通过实验证明了主观数据库系统可以更有效地满足用户需求。