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.

中文翻译：

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MORTAL：通过强化学习为多模型数据自动设计关系存储模式的工具

考虑到关系数据库在处理安全性、用户认证、查询优化等方面具有强大的能力，一些商业和学术框架重用关系数据库来存储和查询半结构化数据（如 XML、JSON）或图数据（如 RDF、属性图）。然而，这些工作集中在使用 RDBMS 管理上述数据模型之一。也就是说，它没有利用底层工具来自动生成用于存储多模型数据的关系模式。在本演示中，我们展示了一种名为 MORTAL 的新型基于强化学习的工具。具体来说，给定包含不同数据模型和一组查询的多模型数据，它可以自动设计一个关系模式来存储这些数据，同时具有很好的查询性能。为了清楚地证明它，