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Storing Multi-model Data in RDBMSs based on Reinforcement Learning
arXiv - CS - Databases Pub Date : 2021-09-01 , DOI: arxiv-2109.00141
Gongsheng Yuan, Jiaheng Lu, Shuxun Zhang, Zhengtong Yan

How to manage various data in a unified way is a significant research topic in the field of databases. To address this problem, researchers have proposed multi-model databases to support multiple data models in a uniform platform with a single unified query language. However, since relational databases are predominant in the current market, it is expensive to replace them with others. Besides, due to the theories and technologies of RDBMSs having been enhanced over decades, it is hard to use few years to develop a multi-model database that can be compared with existing RDBMSs in handling security, query optimization, transaction management, etc. In this paper, we reconsider employing relational databases to store and query multi-model data. Unfortunately, the mismatch between the complexity of multi-model data structure and the simplicity of flat relational tables makes this difficult. Against this challenge, we utilize the reinforcement learning (RL) method to learn a relational schema by interacting with an RDBMS. Instead of using the classic Q-learning algorithm, we propose a variant Q-learning algorithm, called \textit{Double Q-tables}, to reduce the dimension of the original Q-table and improve learning efficiency. Experimental results show that our approach could learn a relational schema outperforming the existing multi-model storage schema in terms of query time and space consumption.

中文翻译:

基于强化学习在 RDBMS 中存储多模型数据

如何统一管理各种数据是数据库领域的一个重要研究课题。为了解决这个问题,研究人员提出了多模型数据库,以在具有单一统一查询语言的统一平台中支持多个数据模型。但是,由于关系数据库在当前市场上占主导地位,因此将其替换为其他数据库的成本很高。此外,由于 RDBMS 的理论和技术经过几十年的改进,很难用几年时间开发出一个多模型数据库,可以在处理安全性、查询优化、事务管理等方面与现有的 RDBMS 相媲美。在本文中,我们重新考虑使用关系数据库来存储和查询多模型数据。很遗憾,多模型数据结构的复杂性与扁平关系表的简单性之间的不匹配使这变得困难。针对这一挑战,我们利用强化学习 (RL) 方法通过与 RDBMS 交互来学习关系模式。我们没有使用经典的 Q-learning 算法,而是提出了一种变体 Q-learning 算法,称为 \textit{Double Q-tables},以降低原始 Q-table 的维度,提高学习效率。实验结果表明,我们的方法可以学习在查询时间和空间消耗方面优于现有多模型存储模式的关系模式。我们没有使用经典的 Q-learning 算法,而是提出了一种变体 Q-learning 算法,称为 \textit{Double Q-tables},以降低原始 Q-table 的维度,提高学习效率。实验结果表明,我们的方法可以学习在查询时间和空间消耗方面优于现有多模型存储模式的关系模式。我们没有使用经典的 Q-learning 算法,而是提出了一种变体 Q-learning 算法,称为 \textit{Double Q-tables},以降低原始 Q-table 的维度,提高学习效率。实验结果表明,我们的方法可以学习在查询时间和空间消耗方面优于现有多模型存储模式的关系模式。
更新日期:2021-09-02
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