Functional Dependencies Analyse in Fuzzy Relational Database Models

Abstract

This paper presents a literature overview of Fuzzy Relational Database Models with emphasis on the role of functional dependencies in logical designing and modeling. The aim is the analysis of recent results in this fi eld. Fuzzy set theory is widely applied for the classical relational database extensions resulting in numerous contributions. This is because fuzzy sets and fuzzy logic are powerful tool for manilupating imprecise and uncertain information. A signifi cant body of research in effi cient designing FRDM has been developed over the last decades. Knowing the set of functional dependencies, database managers have a chance to normalize the same eliminating redundancy and data anomalies. In this paper we have considered the most important results in this fi eld.

Synergy of linguistic summaries and fuzzy functional dependencies for mining knowledge in the data

“…Incorporating techniques from fuzzy logic, concept of FDs is extended to the FFDs [3]. Several approaches have been applied to define FFDs [15,21]. Generally, FFD is described as X   Y, where θ is the strength of dependency [17].…”

“…Keeping in mind the aforementioned points, Fuzzy Functional Dependencies (FFDs) [17,20,21] and Linguistic Summaries (LSs) [8,11,16] could provide the solution. FFDs are able to detect attributes that have a high value of dependency considering their respective whole domains.…”

Fuzzy Functional Dependencies as a Method of Choice for Fusion of AIS and OTHR Data

“…Different similarity functions are employed to compare values of considered attributes. In the last two decades several definitions of FFDs have been introduced [23]. Recently, a new method has been developed for computing FFDs by [24].…”

Some issues of fuzzy querying in relational databases

“…Another approach, adding a semantically similar attribute to the query condition to obtain more restrictive solution is suggested in [4]. In this direction fuzzy functional dependencies [26,54] could be useful in the process of mining related attributes.…”