Patterns on data described by vague limits, vague colimits and vague commutativity

TL;DR

This paper extends algebraic specification methods to model vague data structures and patterns, aiming to improve the formal framework for AI and machine learning processes.

Contribution

It introduces the concept of vague limits, colimits, and commutativity to algebraic specifications, enabling the modeling of vague data patterns.

Findings

Extended algebraic frameworks to vague structures

Defined vague limits and colimits for data modeling

Potential applications in AI data specification

Abstract

The development of machine learning in particular and artificial intelligent in general has been strongly conditioned by the lack of an appropriated framework to specify and integrate learning processes, data transformation processes and data models. In this work we extend traditional algebraic specification methods to this type of framework. Limits and colimits of diagrams are universal constructions fundamental in different mathematical domains importance in semantic modeling. The aim of our work is to study the possibility of extending these algebraic frameworks to the specification of vague structures and to the description of vague patterns on data.