Using Fuzzy Logic to Evaluate Normalization Completeness for An Improved Database Design

TL;DR

This paper introduces a fuzzy logic-based method to quantitatively evaluate the normalization completeness of database schemas, enhancing traditional normalization assessment techniques.

Contribution

It proposes a novel fuzzy logic approach to measure normalization completeness, integrating quantitative analysis into conceptual database design evaluation.

Findings

Fuzzy membership values effectively scale normalization levels.

The method accurately identifies schema transformation points.

Case studies demonstrate practical applicability.

Abstract

A new approach, to measure normalization completeness for conceptual model, is introduced using quantitative fuzzy functionality in this paper. We measure the normalization completeness of the conceptual model in two steps. In the first step, different normalization techniques are analyzed up to Boyce Codd Normal Form (BCNF) to find the current normal form of the relation. In the second step, fuzzy membership values are used to scale the normal form between 0 and 1. Case studies to explain schema transformation rules and measurements. Normalization completeness is measured by considering completeness attributes, preventing attributes of the functional dependencies and total number of attributes such as if the functional dependency is non-preventing then the attributes of that functional dependency are completeness attributes. The attributes of functional dependency which prevent to go to the next normal form are called preventing attributes.