Flexible queries on relational databases using fuzzy logic and ontologies

2016 ◽  
Vol 366 ◽  
pp. 150-164 ◽  
Author(s):  
Carmen Martínez-Cruz ◽  
José M. Noguera ◽  
M. Amparo Vila
Keyword(s):  
Fuzzy Logic ◽  
Relational Databases ◽  
Flexible Queries

Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining

2014 ◽  
pp. 174-203 ◽  
Author(s):  
Miroslav Hudec ◽  
Miljan Vučetić ◽  
Mirko Vujošević
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Relational Databases ◽  
Missing Values ◽  
Expert Knowledge ◽  
Real Data ◽  
Research Area ◽  
Functional Dependencies ◽  
Useful Knowledge ◽  
Important Research Area

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and representation of expert knowledge. Fuzzy functional dependencies could detect whether dependency between two examined attributes in the whole database exists. If dependency exists only between parts of examined attributes' domains, fuzzy functional dependencies cannot detect its characters. Linguistic summaries are a convenient method for revealing this kind of dependency. Using fuzzy functional dependencies and linguistic summaries in a complementary way could mine valuable information from relational databases. Mining intensities of dependencies between database attributes could support decision making, reduce the number of attributes in databases, and estimate missing values. The proposed approach is evaluated with case studies using real data from the official statistics. Strengths and weaknesses of the described methods are discussed. At the end of the chapter, topics for further research activities are outlined.

A Fuzzy Ontology-Based Platform for Flexible Querying

2015 ◽  
Vol 6 (3) ◽  
pp. 12-26 ◽  
Author(s):  
Amira Aloui ◽  
Amel Grissa Touzi
Keyword(s):  
Fuzzy Logic ◽  
Initial Data ◽  
Data Retrieval ◽  
Conceptual Clustering ◽  
Large Databases ◽  
Flexible Queries ◽  
Definition Of ◽  
Preliminary Classification ◽  
Flexible Querying

Flexible queries have recently received increasing attention to better characterize the data retrieval. In this paper, a new flexible querying approach using ontological knowledge is proposed. This approach presents an FCA based methodology for building ontologies from scratch then interrogating them intelligently through the fusion of conceptual clustering, fuzzy logic, and FCA. The main contribution is the definition of the ontology rom classes resulting from a preliminary classification of the data and not the initial data. The data cleansing provides a simple ontology and an optimal research of relevant data taking into account the preferences cited by the user in his initial interrogation. To realize this approach, a new platform called “FO-FQ Tab plug-in” is implemented. This plug-in is integrated within the ontology editor Protégé to allow building fuzzy ontologies from large databases and querying them intelligently

FSQL and SQLf

2011 ◽  
pp. 270-298 ◽  
Author(s):  
Angélica Urrutia ◽  
Leonid Tineo ◽  
Claudia Gonzalez
Keyword(s):  
Fuzzy Logic ◽  
System Architecture ◽  
Relational Databases ◽  
Experimental Performance ◽  
Satisfaction Degree

Actually, FSQL and SQLf are the main fuzzy logic based proposed extensions to SQL. It would be very interesting to integrate them with a standard for fuzzy databases. The issue is what to take from one or other proposal. In this chapter, we analyze FSQL and SQLf making a comparison in several ways: approach direction, fuzzy components, system architecture, satisfaction degree, evaluation mechanisms, and experimental performance. We observe that there are powerful and interesting features in both proposals that could be mixed in a unified language for fuzzy relational databases.

Formalising PFSQL queries using ŁΠ fuzzy logic

2011 ◽  
Vol 22 (3) ◽  
pp. 533-547 ◽  
Author(s):  
ALEKSANDAR PEROVIĆ ◽  
ALEKSANDAR TAKAČI ◽  
SRDJAN ŠKRBIĆ
Keyword(s):  
Fuzzy Logic ◽  
Constraint Satisfaction ◽  
Relational Databases ◽  
Query Language ◽  
Constraint Satisfaction Problem ◽  
Current Paper ◽  
Conservative Extension ◽  
Logic Formula ◽  
Structured Query Language

Using the concept of a generalised priority constraint satisfaction problem, we previously found a way to introduce priority queries into fuzzy relational databases. The results were PFSQL (Priority Fuzzy Structured Query Language) together with a database independent interpreter for it. In an effort to improve the performance of the resolution of PFSQL queries, the aim of the current paper is to formalise PFSQL queries by obtaining their interpretation in an existing fuzzy logic. We have found that the ŁΠ logic provides sufficient elements. The SELECT line of PFSQL queries is semantically a formula of some fuzzy logic, and we show that such formulas can be naturally expressed in a conservative extension of the ŁΠ logic. Furthermore, we prove a theorem that gives the PSPACE containment for the complexity of finding a model for a given ŁΠ logic formula.

A Fuzzy Logic Approach for the Assessment of Online Customers

2012 ◽  
pp. 252-270
Author(s):  
Nicolas Werro ◽  
Henrik Stormer
Keyword(s):  
Fuzzy Logic ◽  
Global Economy ◽  
Relational Databases ◽  
Customer Relationships ◽  
Customer Equity ◽  
Fuzzy Classification ◽  
Loyalty Programs ◽  
Fuzzy Logic Approach ◽  
Logic Approach

A key challenge for companies in the e-business era is to manage customer relationships as an asset. In today’s global economy this task is becoming simultaneously more difficult and more important. In order to retain the potentially good customers and to improve their buying attitude, this chapter proposes a hierarchical fuzzy classification of online customers. A fuzzy classification, which is a combination of relational databases and fuzzy logic, allows customers to be classified into several classes at the same time and can therefore precisely determine the customers’ value for an enterprise. This approach allows companies to improve the customer equity, to launch loyalty programs, to automate mass customization, and to refine marketing campaigns in order to maximize the customers’ value and, this way, the companies’ profit.

Prioritized fuzzy logic based information processing in relational databases

2013 ◽  
Vol 38 ◽  
pp. 62-73 ◽  
Author(s):  
Srdjan Škrbić ◽  
Miloš Racković ◽  
Aleksandar Takači
Keyword(s):  
Fuzzy Logic ◽  
Information Processing ◽  
Relational Databases

scholarly journals Towards the methodology for development of fuzzy relational database applications

2011 ◽  
Vol 8 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Srdjan Skrbic ◽  
Milos Rackovic ◽  
Aleksandar Takaci
Keyword(s):  
Fuzzy Logic ◽  
Relational Database ◽  
Data Model ◽  
Relational Databases ◽  
Model Development ◽  
Relational Data ◽  
Database Applications ◽  
Case Tool ◽  
Relational Data Model ◽  
Fuzzy Database

In this paper we examine the possibilities to extend the relational data model with the mechanisms that can handle imprecise, uncertain and inconsistent attribute values using fuzzy logic and fuzzy sets. We present a fuzzy relational data model which we use for fuzzy knowledge representation in relational databases that guarantees the model in 3rd normal form. We also describe the CASE tool for the fuzzy database model development which is apparently the first implementation of such a CASE tool. In this sense, this paper presents a leap forward towards the specification of a methodology for fuzzy relational database applications development.

scholarly journals Flexible queries in relational databases — The example of the division operator

1997 ◽  
Vol 171 (1-2) ◽  
pp. 281-302 ◽  
Author(s):  
Patrick Bosc ◽  
Didier Dubois ◽  
Olivier Pivert ◽  
Henri Prade
Keyword(s):  
Relational Databases ◽  
Flexible Queries

Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining

2015 ◽  
pp. 1913-1940
Author(s):  
Miroslav Hudec ◽  
Miljan Vučetić ◽  
Mirko Vujošević
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Relational Databases ◽  
Missing Values ◽  
Expert Knowledge ◽  
Real Data ◽  
Research Area ◽  
Functional Dependencies ◽  
Useful Knowledge ◽  
Important Research Area

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and representation of expert knowledge. Fuzzy functional dependencies could detect whether dependency between two examined attributes in the whole database exists. If dependency exists only between parts of examined attributes' domains, fuzzy functional dependencies cannot detect its characters. Linguistic summaries are a convenient method for revealing this kind of dependency. Using fuzzy functional dependencies and linguistic summaries in a complementary way could mine valuable information from relational databases. Mining intensities of dependencies between database attributes could support decision making, reduce the number of attributes in databases, and estimate missing values. The proposed approach is evaluated with case studies using real data from the official statistics. Strengths and weaknesses of the described methods are discussed. At the end of the chapter, topics for further research activities are outlined.

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