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Considering relational databases having powerful capabilities in handlingsecurity, user authentication, query optimization, etc., several commercial andacademic frameworks reuse relational databases to store and querysemi-structured data (e.g., XML, JSON) or graph data (e.g., RDF, propertygraph). However, these works concentrate on managing one of the above datamodels with RDBMSs. That is, it does not exploit the underlying tools toautomatically generate the relational schema for storing multi-model data. Inthis demonstration, we present a novel reinforcement learning-based tool calledMORTAL. Specifically, given multi-model data containing different data modelsand a set of queries, it could automatically design a relational schema tostore these data while having a great query performance. To demonstrate itclearly, we are centered around the following modules: generating initial statebased on loaded multi-model data, influencing learning process by settingparameters, controlling generated relational schema through providing semanticconstraints, improving the query performance of relational schema by specifyingqueries, and a highly interactive interface for showing query performance andstorage consumption when users adjust the generated relational schema.

MORTAL: A Tool of Automatically Designing Relational Storage Schemas for Multi-model Data through Reinforcement Learning