Considering relational databases having powerful capabilities in handling security, user authentication, query optimization, etc., several commercial and academic frameworks reuse relational databases to store and query semi-structured data (e.g., XML, JSON) or graph data (e.g., RDF, property graph). However, these works concentrate on managing one of the above data models with RDBMSs. That is, it does not exploit the underlying tools to automatically generate the relational schema for storing multi-model data. In this demonstration, we present a novel reinforcement learning-based tool called MORTAL. Specifically, given multi-model data containing different data models and a set of queries, it could automatically design a relational schema to store these data while having a great query performance. To demonstrate it clearly, we are centered around the following modules: generating initial state based on loaded multi-model data, influencing learning process by setting parameters, controlling generated relational schema through providing semantic constraints, improving the query performance of relational schema by specifying queries, and a highly interactive interface for showing query performance and storage consumption when users adjust the generated relational schema.
翻译:考虑到具有处理安全、用户认证、查询优化等强大能力的关系数据库,若干商业和学术框架,重新利用关系数据库,储存和查询半结构数据(例如XML、JSON)或图表数据(例如RDF、属性图),然而,这些工作集中于管理上述数据模型中与RDBMS系统有关的一个数据模型。也就是说,它没有利用基本工具自动生成储存多模型数据的关系模型。在这个演示中,我们提出了一个新的强化学习工具,称为MORTAL。具体地说,由于有包含不同数据模型和一组查询的多模型数据,它可以自动设计一种关系模型,储存这些数据,同时具有很强的查询性能。为了明确证明这一点,我们围绕以下模块开展工作:根据已装载的多模型数据生成初始状态,通过设定参数来影响学习过程,通过提供语系限制来控制产生的关系模型,通过具体说明查询来改进关系模型的查询性能,以及用户在调整关系模型时显示查询性能和存储消耗的高度互动界面。