Categorical Database for Ensuring Data Integrity in Institutional Research

Abstract

Institutional research deals with large and different datasets from various departments, and this can make it hard to keep the data accurate. In this paper, we present categorical databases, which is a method based on category theory, that helps to maintain data integrity. By organizing the database as a category, we can see how the data elements are connected. This makes it easier to do meaningful and precise data analysis. The connections between data can be shown as simple sentences that still make sense, even when the data is updated. This way ensures that data remain consistent in both databases and data warehouses through natural transformations, which means that references to the data stay trustworthy. Categorical databases provide a solid way to manage complex data structures, and they make sure that data integrity is kept.