Juan C. Cubero

A Server for Fuzzy SQL Queries

The client-server model is being used mostly in the actual DataBase Management Systems (DBMS). However, these DBMS do not allow either to make flexible queries to the database or to store vague information in it. We have developed a FSQL Server for a Fuzzy Relational Database (FRDB). The FSQL language (Fuzzy SQL) is an extension of the SQL language that allows us to write flexible conditions in our queries. This Server has been developed for Oracle, following the model GEFRED, a theoric model for FRDB that includes fuzzy attributes to store vague information in the tables. The FSQL Server allows us to make flexible queries about traditional (crisp) or fuzzy attributes and we can use linguistic labels defined on any attribute.

Relaxing the Universal Quantifier of the Division in Fuzzy Relational Databases

In a previous paper, we presented an approach to calculate relational division in fuzzy databases, starting with the GEFRED model. This work centered on dealing with fuzzy attributes and fuzzy values and only the universal quantifier was taken into account since it is the inherent quantifier in classical relational division. In this paper, we present an extension of that division to relax the universal quantifier. With this new system we can use both absolute quantifiers and relative quantifiers irrespective of how the function of the fuzzy quantifier is defined. We also include a comparison with other fuzzy division approaches to relax the universal quantifier that have been published. Furthermore, in this paper we have extended the fuzzy SQL language to express any kind of fuzzy division. © 2001 John Wiley & Sons, Inc.

Influence of Granularity Level in Fuzzy Functional Dependencies

Relational Databases (R.D) can be considered the most widely used approach to Databases [5]. But in Classical R.D it is not possible to store or treat vague information.

Rules discovery in fuzzy relational databases

We are concerned with the problem of defining a fuzzy dependency in the framework of a fuzzy relational database. Of prime interest is the development of a fuzzy algebra operator, which allows us to store that information in a separated relation with fewer tuples. Its definition can be carried out through a crisp criterion or through a fuzzy one: we study the necessary restrictions for each case.

Soft computing: A new perspective for some data mining problems

Abstract Databases in the real world are often dynamic, incomplete, noisy and very large and most of the problems which appear in the Data Mining field are caused by these characteristics. However, it is often unnecessary to obtain results (rules, clusters, etc.) with a high degree of precision since the input data is itself imprecise. For these reasons, we propose a new approach to cope with these situations. We refer to the “Soft Computing” methodology which combines the ability of Fuzzy Logic to represent and manage imprecise data and knowledge together with the accepted capacities for learning and heuristic computation of Neural Networks and Genetic Algorithms. The aim of the paper is to present some of the possiblities offered by the use of Soft Computing.

An Implementation for Fuzzy Deductive Relational Databases

This chapter shows how to integrate the representation of deductive rules and fuzzy information stored in a relational DBMS to build a module that can obtain new data from data stored in tables. The deductions can be applied to classical (or precise) data, imprecise data or both of them, so it is necessary to provide a mechanism to find the tuples in the database satisfying a rule, i.e. a mechanism to calculate the precision degree of the answer by means of the combination of the precision degrees of every value into an unified measure. Keywords, relational databases extension, fuzzy deduction, inference.

An Architecture for a Deductive Fuzzy Relational Database

This paper reports on the architecture of a Fuzzy Relational DBMS (FRDBMS) with deduction capabilities, whose main characteristics are: 1) It is built on the basis of a theoretical model for fuzzy relational databases and a theoretical model for logic fuzzy databases; 2) It is implemented entirely on classical RDBMS, using their resources; 3) It conserves all the operations of the host RDBMS and gives them more power, adding new capabilities for dealing with ”fuzzy” and ”intensive” information; 4) It provides a deductive fuzzy language, DFSQL, and a processor which permits the translation of each DFSQL statement into one or more SQL statements, which can be used by the host RDBMS; 5) It offers a relational representaion of the rules that define an intensive table, in such a way that all necessary information to perform deduction is stored in tables. 6) This system needs to interact with a deduction module which performs the computation of intensive tables.

A fuzzy deductive relational database

This paper presents the architecture of a Relational DBMS which uses deduction capabilities for handling imprecise information. The main characteristics of the proposed architecture are: 1) It is implemented entirely using classical RDBMS resources, 2) It conserves all the operations of the host RDBMS and gives them more power, adding new capabilities for dealing with “fuzzy” and “intensional” information; 3) It provides a deductive fuzzy language, DFSQL, and a processor which permits the translation of each DFSQL statement into one or more SQL statements, which can be processed by the host RDBMS; 4) It offers a relational representation of the rules that define medical concepts, in such a way that all necessary information to perform deduction is stored in tables.

The ModelCC Model-Driven Parser Generator.

Syntax-directed translation tools require the specification of a language by means of a formal grammar. This grammar must conform to the specific requirements of the parser generator to be used. This grammar is then annotated with semantic actions for the resulting system to perform its desired function. In this paper, we introduce ModelCC, a model-based parser generator that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual syntax and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a reference resolution mechanism so that ModelCC is able to instantiate abstract syntax graphs, rather than mere abstract syntax trees.

Computing Fuzzy Dependencies with Linguistic Labels

The relational database model is the most widely used in commercial systems. When we design a database, we must choose the attributes and properties that should appear in every relation. For this task, the concept of functional dependency (f.d) is a fundamental issue: roughly, the attributes which do not appear in a candidate key should not verify any kind of f.d. We extend this notion, for the case when the dependencies are not crisp but fuzzy. The use of linguistic labels will play a fundamental role in our approximation, so we advocate the spirit of computing with words in Zadeh’s sense.