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

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.

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“…3. and work that does not deal with simple values (fuzzy databases, where values can be fuzzy functions) [25,26,13,23,5].…”

Application of Fuzzy Logic for User Classification in Personalized Web Search

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“…In order to illustrate uncertainty and vagueness mathematically, fuzzy concept is specifically designed. Everything is a matter of degree in fuzzy logic and knowledge is interpreted as a collection of elastic or equally fuzzy constraint on a collection of variables [32][33][34][35][36][37][38]. Concept of Linguistic variable plays an important role in fuzzy logic and their values are words or sentences in natural language [39,40].…”

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