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This volume presents the results of approximately 15 years of work from researchers around the world on the use of fuzzy set theory to represent imprecision in databases. The maturity of the research in the discipline and the recent developments in commercial/industrial fuzzy databases provided an opportunity to produce this survey. In this introduction we will describe briefly how fuzzy databases fit into the overall design of database systems and then overview the organization of the text. FUZZY DATABASE LANDSCAPE The last five years have been witness to a revolution in the database research community. The dominant data models have changed and the consensus on what constitutes worthwhile research is in flux. Also, at this time, it is possible to gain a perspective on what has been accomplished in the area of fuzzy databases. Therefore, now is an opportune time to take stock of the past and establish a framework. A framework should assist in evaluating future research through a better understanding of the different aspects of imprecision that a database can model [ 1 l.
"This book includes an introduction to fuzzy logic, fuzzy databases and an overview of the state of the art in fuzzy modeling in databases"--Provided by publisher.
Since their inception, fuzzy sets and fuzzy logic became popular. The reason is that the very idea of fuzzy sets and fuzzy logic attacks an old tradition in science, namely bivalent (black-or-white, all-or-none) judg ment and reasoning and the thus resulting approach to formation of scientific theories and models of reality. The idea of fuzzy logic, briefly speaking, is just the opposite of this tradition: instead of full truth and falsity, our judgment and reasoning also involve intermediate truth values. Application of this idea to various fields has become known under the term fuzzy approach (or graded truth approach). Both prac tice (many successful engineering applications) and theory (interesting nontrivial contributions and broad interest of mathematicians, logicians, and engineers) have proven the usefulness of fuzzy approach. One of the most successful areas of fuzzy methods is the application of fuzzy relational modeling. Fuzzy relations represent formal means for modeling of rather nontrivial phenomena (reasoning, decision, control, knowledge extraction, systems analysis and design, etc. ) in the pres ence of a particular kind of indeterminacy called vagueness. Models and methods based on fuzzy relations are often described by logical formulas (or by natural language statements that can be translated into logical formulas). Therefore, in order to approach these models and methods in an appropriate formal way, it is desirable to have a general theory of fuzzy relational systems with basic connections to (formal) language which enables us to describe relationships in these systems.
"This book provides comprehensive coverage and definitions of the most important issues, concepts, trends, and technologies in fuzzy topics applied to databases, discussing current investigation into uncertainty and imprecision management by means of fuzzy sets and fuzzy logic in the field of databases and data mining. It offers a guide to fuzzy information processing in databases"--Provided by publisher.
also in: THE KLUWER INTERNATIONAL SERIES ON ASIAN STUDIES IN COMPUTER AND INFORMATION SCIENCE, Volume 2
Fuzzy Database Modeling with XML aims to provide a single record of current research and practical applications in the fuzzy databases. This volume is the outgrowth of research the author has conducted in recent years. Fuzzy Database Modeling with XML introduces state-of-the-art information to the database research, while at the same time serving the information technology professional faced with a non-traditional application that defeats conventional approaches. The research on fuzzy conceptual models and fuzzy object-oriented databases is receiving increasing attention, in addition to fuzzy relational database models. With rapid advances in network and internet techniques as well, the databases have been applied under the environment of distributed information systems. It is essential in this case to integrate multiple fuzzy database systems. Since databases are commonly employed to store and manipulate XML data, additional requirements are necessary to model fuzzy information with XML. Secondly, this book maps fuzzy XML model to the fuzzy databases. Very few efforts at investigating these issues have thus far occurred. Fuzzy Database Modeling with XML is designed for a professional audience of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science.
The manipulation of databases is an integral part of a world which is becoming increasingly and pervasively information-focused. This book puts forward a suggestion to advocate preference queries and fuzzy sets as a central concern in database queries and offers an important contribution to the design of intelligent information systems. It provides a comprehensive study on fuzzy preference queries in the context of relational databases. Preference queries, a recent hot topic in database research, provide a basis for rank-ordering the items retrieved, which is especially valuable for large sets of answers.This book aims to show that fuzzy set theory constitutes a highly expressive framework for modeling preference queries. It presents a study of the algorithmic aspects related to the evaluation of such queries in order to demonstrate that this framework offers a good trade-off between expressivity and efficiency. Numerous examples and proofs are liberally and lucidly demonstrated throughout, and greatly enhance the detailed theoretical aspects explored in the book.Researchers working in databases will greatly benefit from this comprehensive and up-to-date study of fuzzy preference queries, and it will also become an invaluable reference point for postgraduate students interested in advanced database techniques.The only other books which deal with this topic are edited books or conference proceedings which include a few contributions about some specific aspects of the question. This book provides a comprehensive view of the issue, starting with basic notions related to relational databases and fuzzy set theory, up to the detailed study of complex fuzzy queries and the way they can be efficiently processed. It is the compendium of more than 20 years of research by the authors who benefit from a great international recognition in the domain of intelligent information systems, on the subject.
Until this book, the available literature on fuzzy sets has been, at best, scattered throughout industrial and university libraries. Encapsulated here is a sound discussion of the basic theoretical and practical aspects involved in fuzzy database systems. With a practical, hands-on, applications-oriented approach, it develops computer models for applications to decision-making processes, introducing the basic notion of relative grades via the fuzzy set theoretic approach. Also covers fuzzy relational databases and their calculus, and the fuzzy relational (structured) query language (FSQL). The last sections present methods for treating the incomplete information in fuzzy PROLOG database (FPDB) systems. Several examples of knowledge representation, expert systems, fuzzy control, and fuzzy clustering and information retrieval illustrate the theory. An extended sample database is used throughout the book.
Computer-based information technologies have been extensively used to help industries manage their processes and information systems hereby - come their nervous center. More specially, databases are designed to s- port the data storage, processing, and retrieval activities related to data management in information systems. Database management systems p- vide efficient task support and database systems are the key to impleme- ing industrial data management. Industrial data management requires da- base technique support. Industrial applications, however, are typically data and knowledge intensive applications and have some unique character- tics that makes their management difficult. Besides, some new techniques such as Web, artificial intelligence, and etc. have been introduced into - dustrial applications. These unique characteristics and usage of new te- nologies have put many potential requirements on industrial data mana- ment, which challenge today’s database systems and promote their evolvement. Viewed from database technology, information modeling in databases can be identified at two levels: (conceptual) data modeling and (logical) database modeling. This results in conceptual (semantic) data model and logical database model. Generally a conceptual data model is designed and then the designed conceptual data model will be transformed into a chosen logical database schema. Database systems based on logical database model are used to build information systems for data mana- ment. Much attention has been directed at conceptual data modeling of - dustrial information systems. Product data models, for example, can be views as a class of semantic data models (i. e.
1. When I was asked by the editors of this book to write a foreword, I was seized by panic. Obviously, neither I am an expert in Knowledge Representation in Fuzzy Databases nor I could have been beforehand unaware that the book's contributors would be some of the most outstanding researchers in the field. However, Amparo Vila's gentle insistence gradually broke down my initial resistance, and panic then gave way to worry. Which paving stones did I have at my disposal for making an entrance to the book? After thinking about it for some time, I concluded that it would be pretentious on my part to focus on the subjects which are dealt with directly in the contributions presented, and that it would instead be better to confine myself to making some general reflections on knowledge representation given by imprecise information using fuzzy sets; reflections which have been suggested to me by some words in the following articles such as: graded notions, fuzzy objects, uncertainty, fuzzy implications, fuzzy inference, empty intersection, etc.