  The variety of data is one of the important issues in the era of Big Data.
The data are naturally organized in different formats and models, including
structured data, semi-structured data, and unstructured data. Prior research
has envisioned an approach to abstract multi-model data with a schema category
and an instance category by using category theory. In this paper, we
demonstrate a system, called MultiCategory, which processes multi-model queries
based on category theory and functional programming. This demo is centered
around four main scenarios to show a tangible system. First, we show how to
build a schema category and an instance category by loading different models of
data, including relational, XML, key-value, and graph data. Second, we show a
few examples of query processing by using the functional programming language
Haskell. Third, we demo the flexible outputs with different models of data for
the same input query. Fourth, to better understand the category theoretical
structure behind the queries, we offer a variety of graphical hooks to explore
and visualize queries as graphs with respect to the schema category, as well as
the query processing procedure with Haskell.
