Fuzzy Logic In Data Modeling

also in: THE KLUWER INTERNATIONAL SERIES ON ASIAN STUDIES IN COMPUTER AND INFORMATION SCIENCE, Volume 2

Technology has dramatically changed the way in which knowledge is shared within and outside of traditional classroom settings. The application of fuzzy logic to new forms of technology-centered education has presented new opportunities for analyzing and modeling learner behavior. Fuzzy Logic-Based Modeling in Collaborative and Blended Learning explores the application of the fuzzy set theory to educational settings in order to analyze the learning process, gauge student feedback, and enable quality learning outcomes. Focusing on educational data analysis and modeling in collaborative and blended learning environments, this publication is an essential reference source for educators, researchers, educational administrators and designers, and IT specialists. This premier reference monograph presents key research on educational data analysis and modeling through the integration of research on advanced modeling techniques, educational technologies, fuzzy concept maps, hybrid modeling, neuro-fuzzy learning management systems, and quality of interaction.

"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.

The world we live in is pervaded with uncertainty and imprecision. Is it likely to rain this afternoon? Should I take an umbrella with me? Will I be able to find parking near the campus? Should I go by bus? Such simple questions are a c- mon occurrence in our daily lives. Less simple examples: What is the probability that the price of oil will rise sharply in the near future? Should I buy Chevron stock? What are the chances that a bailout of GM, Ford and Chrysler will not s- ceed? What will be the consequences? Note that the examples in question involve both uncertainty and imprecision. In the real world, this is the norm rather than exception. There is a deep-seated tradition in science of employing probability theory, and only probability theory, to deal with uncertainty and imprecision. The mon- oly of probability theory came to an end when fuzzy logic made its debut. H- ever, this is by no means a widely accepted view. The belief persists, especially within the probability community, that probability theory is all that is needed to deal with uncertainty. To quote a prominent Bayesian, Professor Dennis Lindley, “The only satisfactory description of uncertainty is probability.

Some recent fuzzy database modeling advances for the non-traditional applications are introduced in this book. The focus is on database models for modeling complex information and uncertainty at the conceptual, logical, physical design levels and from integrity constraints defined on the fuzzy relations. The database models addressed here are; the conceptual data models, including the ExIFO and ExIFO2 data models, the logical database models, including the extended NF2 database model, fuzzy object-oriented database model, and the fuzzy deductive object-oriented database model. Integrity constraints are defined on the fuzzy relations are also addressed. A continuing reason for the limited adoption of fuzzy database systems has been performance. There have been few efforts at defining physical structures that accomodate fuzzy information. A new access structure and data organization for fuzzy information is introduced in this book.

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.

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.

The volume "Fuzziness in Database Management Systems" is a highly informative, well-organized and up-to-date collection of contributions authored by many of the leading experts in its field. Among the contributors are the editors, Professors Patrick Bose and Janusz Kacprzyk, both of whom are known internationally. The book is like a movie with an all-star cast. The issue of fuzziness in database management systems has a long history. It begins in 1968 and 1971, when I spent my sabbatical leaves at the IBM Research Laboratory in San Jose, California, as a visiting scholar. During these periods I was associated with Dr. E.F. Codd, the father of relational models of database systems, and came in contact with the developers ofiBMs System Rand SQL. These associations and contacts at a time when the methodology of relational models of data was in its formative stages, made me aware of the basic importance of such models and the desirability of extending them to fuzzy database systems and fuzzy query languages. This perception was reflected in my 1973 ffiM report which led to the paper on the concept of a linguistic variable and later to the paper on the meaning representation language PRUF (Possibilistic Relational Universal Fuzzy). More directly related to database issues during that period were the theses of my students V. Tahani, J. Yang, A. Bolour, M. Shen and R. Sheng, and many subsequent reports by both graduate and undergraduate students at Berkeley.