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.

中文翻译：

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MultiCategory：多模型查询处理满足类别理论和函数式编程

数据的多样性是大数据时代的重要问题之一。数据自然地以不同的格式和模型进行组织，包括结构化数据、半结构化数据和非结构化数据。先前的研究已经设想了一种通过使用类别理论来抽象具有模式类别和实例类别的多模型数据的方法。在本文中，我们展示了一个名为 MultiCategory 的系统，该系统基于类别理论和函数式编程处理多模型查询。该演示以四个主要场景为中心，以展示一个有形的系统。首先，我们展示了如何通过加载不同的数据模型（包括关系、XML、键值和图形数据）来构建模式类别和实例类别。其次，我们展示了一些使用函数式编程语言 Haskell 进行查询处理的示例。第三，我们针对相同的输入查询演示了具有不同数据模型的灵活输出。第四，为了更好地理解查询背后的类别理论结构，我们提供了各种图形挂钩来探索和可视化查询作为关于模式类别的图形，以及使用 Haskell 的查询处理过程。