MultiCategory: Multi-model Query Processing Meets Category Theory and Functional Programming

TL;DR

This paper introduces MultiCategory, a system that leverages category theory and functional programming to process and visualize multi-model data queries across various data formats.

Contribution

It demonstrates a novel approach to multi-model data query processing using category theory and Haskell, with practical visualization tools.

Findings

Built schema and instance categories for relational, XML, key-value, and graph data

Showcased query processing examples using Haskell

Provided visualization tools for query structures and processing steps

Abstract

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.