Fuzzy Data Models and Formal Descriptions

2013 ◽  
pp. 33-60
Author(s):  
Zongmin Ma ◽  
Fu Zhang ◽  
Li Yan ◽  
Jingwei Cheng
Keyword(s):  
Data Models ◽  
Fuzzy Data

An Overview of Fuzzy Data Models

1995 ◽  
pp. 23-41 ◽  
Author(s):  
Etienne E. Kerre ◽  
Guoqing Chen
Keyword(s):  
Data Models ◽  
Fuzzy Data

Modeling and Querying Fuzzy Data

2018 ◽  
pp. 2245-2273
Author(s):  
Li Yan ◽  
Z. M. Ma
Keyword(s):  
Data Management ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Imperfect Information ◽  
Data Models ◽  
Fuzzy Data ◽  
Current State ◽  
Data Querying ◽  
Database Models ◽  
Knowledge Intensive

Imperfect information extensively exists in data and knowledge intensive applications, where fuzzy data play an import role in nature. Fuzzy set theory has been extensively applied to extend various database models and resulted in numerous contributions. The chapter concentrates on two main issues in fuzzy data management: fuzzy data models and fuzzy data querying based on the fuzzy data models. A full up-to-date overview of the current state of the art in fuzzy data modeling and querying is provided in the chapter. In addition, the relationships among various fuzzy data models are discussed in the chapter. The chapter serves as identifying possible research opportunities in the area of fuzzy data management in addition to providing a generic overview of the approaches to modeling and querying fuzzy data.

Modeling and Querying Fuzzy Data

2015 ◽  
pp. 129-157
Author(s):  
Li Yan ◽  
Z. M. Ma
Keyword(s):  
Data Management ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Imperfect Information ◽  
Data Models ◽  
Fuzzy Data ◽  
Current State ◽  
Data Querying ◽  
Database Models ◽  
Knowledge Intensive

Imperfect information extensively exists in data and knowledge intensive applications, where fuzzy data play an import role in nature. Fuzzy set theory has been extensively applied to extend various database models and resulted in numerous contributions. The chapter concentrates on two main issues in fuzzy data management: fuzzy data models and fuzzy data querying based on the fuzzy data models. A full up-to-date overview of the current state of the art in fuzzy data modeling and querying is provided in the chapter. In addition, the relationships among various fuzzy data models are discussed in the chapter. The chapter serves as identifying possible research opportunities in the area of fuzzy data management in addition to providing a generic overview of the approaches to modeling and querying fuzzy data.

scholarly journals Lossless Join Decomposition for Extended Possibility-Based Fuzzy Relational Databases

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Julie Yu-Chih Liu
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Decomposition Method ◽  
Relational Databases ◽  
Data Models ◽  
Functional Dependency ◽  
Fuzzy Data ◽  
Functional Dependencies ◽  
Approximate Equality ◽  
Database Normalization

Functional dependency is the basis of database normalization. Various types of fuzzy functional dependencies have been proposed for fuzzy relational database and applied to the process of database normalization. However, the problem of achieving lossless join decomposition occurs when employing the fuzzy functional dependencies to database normalization in an extended possibility-based fuzzy data models. To resolve the problem, this study defined fuzzy functional dependency based on a notion of approximate equality for extended possibility-based fuzzy relational databases. Examples show that the notion is more applicable than other similarity concept to the research related to the extended possibility-based data model. We provide a decomposition method of using the proposed fuzzy functional dependency for database normalization and prove the lossless join property of the decomposition method.

Information Processing Based on Mixed - Classical and Fuzzy - Data Models

2001 ◽  
Vol 5 (1) ◽  
pp. 44-50
Author(s):  
Orsolya Takács ◽  
◽  
Annamária R. Várkonyi-Kóczy
Keyword(s):  
Information Processing ◽  
Data Model ◽  
Uncertain Data ◽  
Data Models ◽  
Mixed Data ◽  
Fuzzy Data ◽  
Classical Probability ◽  
Mixed Use ◽  
Achievable Accuracy ◽  
Data Processing Methods

The model used to represent information during information processing could affect achievable accuracy and could determine the usability of different calculation methods. The data model must also be able to represent uncertainty and inaccuracy both of input data and results. The two most popular data models for representation of uncertain data is the "classical", probability based, and the recently introduced fuzzy data models. Both data models have their own calculation and data processing methods, but with the increasing complexity of calculation problems, a method for the mixed use of these data models is be needed. This paper deals with possible solutions for information processing based on mixed data models and examines the different conversion methods between fuzzy and probability theory based data models.

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

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