XML Modeling of Fuzzy Data with Relational Databases

2011 ◽  
Vol 34 (2) ◽  
pp. 291-303 ◽  
Author(s):  
Li YAN ◽  
Zong-Min MA ◽  
Jian LIU ◽  
Fu ZHANG
Keyword(s):  
Relational Databases ◽  
Fuzzy Data

How to Achieve Fuzzy Relational Databases Managing Fuzzy Data and Metadata

2011 ◽  
pp. 351-380 ◽  
Author(s):  
Mohamed Ali Ben Hassine ◽  
Amel Grissa Touzi ◽  
José Galindo ◽  
Habib Ounelli
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Fuzzy Data ◽  
Imprecise Data ◽  
The Third ◽  
Fuzzy Database ◽  
Existing Data ◽  
Flexible Querying ◽  
Mining Tools ◽  
Total Migration

Fuzzy relational databases have been introduced to deal with uncertain or incomplete information demonstrating the efficiency of processing fuzzy queries. For these reasons, many organizations aim to integrate flexible querying to handle imprecise data or to use fuzzy data mining tools, minimizing the transformation costs. The best solution is to offer a smooth migration towards this technology. This chapter presents a migration approach from relational databases towards fuzzy relational databases. This migration is divided into three strategies. The first one, named “partial migration,” is useful basically to include fuzzy queries in classic databases without changing existing data. It needs some definitions (fuzzy metaknowledge) in order to treat fuzzy queries written in FSQL language (Fuzzy SQL). The second one, named “total migration,” offers in addition to the flexible querying, a real fuzzy database, with the possibility to store imprecise data. This strategy requires a modification of schemas, data, and eventually programs. The third strategy is a mixture of the previous strategies, generally as a temporary step, easier and faster than the total migration.

Dynamic Discovery of Fuzzy Functional Dependencies Using Partitions

2010 ◽  
pp. 44-60
Author(s):  
Shyue-Liang Wang ◽  
Ju-Wen Shen ◽  
Tuzng-Pei Hong
Keyword(s):  
Relational Databases ◽  
Computation Time ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Functional Dependencies ◽  
Mining Algorithm ◽  
Analysis Technique ◽  
Static Data ◽  
Pair Wise Comparison ◽  
Reduce Computation Time

Discovery of functional dependencies (FDs) from relational databases has been identified as an important database analysis technique. Various mining techniques have been proposed in recent years to deal with crisp and static data. However, few have emphasized on fuzzy data and also considered the dynamic nature that data may change all the time. In this work, the authors propose a partition-based incremental data mining algorithm to discover fuzzy functional dependencies from similarity-based fuzzy relational databases when new sets of tuples are added. Based on the concept of tuple partitions and the monotonicity of fuzzy functional dependencies, we avoid re-scanning of the database and thereby reduce computation time. An example demonstrating the proposed algorithm is given. Computational complexity of the proposed algorithm is analyzed. Comparison with pair-wise comparison-based incremental mining algorithm (Wang, Shen & Hong, 2000) is presented. It is shown that with certain space requirement, partition-based approach is more time efficient than pair-wise approach in the discovery of fuzzy functional dependencies dynamically.

scholarly journals An Ontology as a Tool for Representing Fuzzy Data in Relational Databases

2012 ◽  
Vol 5 (6) ◽  
pp. 1089-1108 ◽  
Author(s):  
Carmen Martinez-Cruz ◽  
Ignacio J. Blanco ◽  
M. Amparo Vila
Keyword(s):  
Relational Databases ◽  
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.

scholarly journals Collecting and managing fuzzy data in statistical relational databases

2016 ◽  
Vol 32 (2) ◽  
pp. 245-255 ◽  
Author(s):  
Miroslav Hudec ◽  
Dušan Praženka
Keyword(s):  
Relational Databases ◽  
Fuzzy Data

VISUALIZATION-BASED SELECTION-AIMED DATA MINING WITH FUZZY DATA

2006 ◽  
Vol 05 (04) ◽  
pp. 611-621 ◽  
Author(s):  
ALEXANDER V. LOTOV
Keyword(s):  
Data Mining ◽  
Financial Planning ◽  
Web Application ◽  
Relational Databases ◽  
Environmental Planning ◽  
Uncertain Data ◽  
Fuzzy Data ◽  
New Development ◽  
The Web ◽  
Machinery Design

The paper is devoted to a visualization-based method for exploration of relational databases that contain large volumes of uncertain data. The visualization is aimed at exploration of properties of the data and selecting a small number of interesting items from the database. The method introduced here is a new development of the Reasonable Goals method, which has already been implemented on Internet in the form of the Web application server. Thus, the new method can be applied on Internet, too. It can be used for selection-aimed data mining in various fields including environmental planning, machinery design, financial planning (including credit operations), biology and medicine.

Looking for Information in Fuzzy Relational Databases Accessible Via Web

2009 ◽  
pp. 2448-2471
Author(s):  
Carmen Martínez-Cruz ◽  
Ignacio José Blanco ◽  
Maria Amparo Vila
Keyword(s):  
Semantic Web ◽  
Relational Databases ◽  
Management Systems ◽  
Database Schema ◽  
Fuzzy Data ◽  
Web Interfaces ◽  
Wide Range ◽  
Fuzzy Database

The Semantic Web has resulted in a wide range of information (e.g., HML, XML, DOC, PDF documents, ontologies, interfaces, forms, etc.) being made available in semantic queries, and the only requirement is that these are described semantically. Generic Web interfaces for querying databases (such as ISQLPlus ©) are also part of the Semantic Web, but they cannot be semantically described, and they provide access to one or many databases. In this chapter, we will highlight the importance of using ontologies to represent database schemas so that they are easier to access. The representation of the fuzzy data in fuzzy databases management systems (FDBMS) has certain special requirements, and these characteristics must be explicitly defined to enable this kind of information to be accessed. In addition, we will present an ontology which allows the fuzzy structure of a fuzzy database schema to be represented so that fuzzy data from FDBMS can also be available in the Semantic Web.

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