  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.
