A conceptual framework for fuzzy query processing—A step toward very intelligent database systems

Recently, fuzzy control has become one of the most attractive areas of fuzzy set theory applications. 3,10 As it provides an effective way to approximate inexact nature of human thinking, this theory is an appropriate tool for converting linguistically expressed expert's knowledge about a given process control into mathematically defined control strategy. This is made by means of the so called linguistic values, which are fuzzy sets over given universe of discourse of corresponding linguistic variable. The membership functions, which describe this linguistic values, as a rule, are defined by some bell-shaped function. Several nonlinear definitions of this functions are known, 2 but most researchers prefer to use simple linear triangular or trapezoidal functions. Usually in fuzzy controller synthesis membership functions shape is defined in advance and then only their position in the universe of discourse is tuned.

Fuzzy sets theory and fuzzy logic constitute the basis for the linguistic approach. Under this approach, variables can assume linguistic values. Each linguistic value is characterized by a label and a meaning. The label is a sentence of a language. The meaning is a fuzzy subset of a universe of discourse. Models, based on this approach, can be constructed to simulate approximate reasoning. The implementation of these models presents two major problems, namely how to associate a label to an unlabelled fuzzy set on the basis of semantic similarity (linguistic approximation) and how to perform arithmetic operations with fuzzy numbers. For each problem a solution is proposed. Two illustrative applications are discussed.

This book consists of 35 chapters presenting different theoretical and practical aspects of Intelligent Information and Database Systems. Nowadays both Intelligent and Database Systems are applied in most of the areas of human activities which necessitates further research in these areas. In this book various interesting issues related to the intelligent information models and methods as well as their advanced applications, database systems applications, data models and their analysis and digital multimedia methods and applications are presented and discussed both from the practical and theoretical points of view. The book is organized in four parts devoted to intelligent systems models and methods, intelligent systems advanced applications, database systems methods and applications and multimedia systems methods and applications. The book will be interesting forpractitioners and researchers, especially graduate and PhD students of information technology and computer science, as well more experienced academics and specialists interested in developing and verification of intelligent information, database and multimedia systems models, methods and applications. The readers of this volume are enabled to find many inspiring ideas and motivating practical examples that will help them in the current and future work.

Nowadays several works have been proposed that allow users to perform fuzzy queries on relational databases. But most of these systems based on an additional software layer to translate a fuzzy query and a supplementary layer of a classic database management system (DBMS) to evaluate fuzzy predicates, which induces an important overhead. They are not also easy to implement by a non-expert user. Here we have proposed a simple and intelligent approach to extend the SQL language to allow us to write flexible conditions in our queries without the need for translation. The main idea is to use a view to manipulate the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating them at runtime employing user functions embedded in the query. Consequently, the response time of executing a fuzzy query statement will be reduced. This approach allows us to easily integrate most fuzzy request characters such as fuzzy modifiers, fuzzy quantifiers, fuzzy joins, etc. Moreover, we present a user-friendly interface to make it easy to use fuzzy linguistic values in all clauses of a select statement. The main contribution of this paper is to accelerate the execution of fuzzy query statements.

In this paper we argue that fuzzy sets and rough sets aim to different purposes and that it is more natural to try to combine the two models of uncertainty (vagueness for fuzzy sets and coarseness for rough sets) in order to get a more accurate account of imperfect information. First, the upper and lower approximations of a fuzzy set are defined, when the universe of discourse of a fuzzy sets is coarsened by means of an equivalence relation. We then come close to Caianiello’s C-calculus. Shafer’s concept of coarsened belief functions also belongs to the same line of thought and is reviewed here. Another idea is to turn the equivalence relation relation into a fuzzy similarity relation, for a more expressive modeling of coarseness. New results on the representation of similarity relations by means of a fuzzy partition of fuzzy clusters of more or less indiscernible points are surveyed. The properties of upper and lower approximations of fuzzy sets by similarity relations are thoroughly studied. Lastly the potential usefulness of the fuzzy rough set notions for logical inference in the presence of both fuzzy predicates and graded indiscernibility is indicated. Especially fuzzy rough sets may provide a nice semantic background for modal logic involving fuzzy modalities and/or fuzzy sentences.

Theories of fuzzy sets and rough sets are powerful mathematical tools for modelling various types of uncertainty. Molodtsov (Comput Math Appl 37:19–31, 1999 [6]) initiated a novel concept called soft sets, a new mathematical tool for dealing with uncertainties. It has been found that fuzzy sets, rough sets, and soft sets are closely related concepts (Aktas and Cagman in Inf Sci 1(77):2726–2735, 2007 [1]). Research works on soft sets are very active and progressing rapidly in these years. In 2001, Maji et al. (J Fuzzy Math 9(3):589–602, 2001 [5]) proposed the idea of intuitionistic fuzzy soft set theory and established some results on them. Based on an equivalence relation on the universe of discourse, Dubois and Prade (Int J Gen Syst 17:191–209, 1990 [3]) introduced the lower and upper approximation of fuzzy sets in a Pawlak approximation space and obtained a new notion called rough fuzzy sets. Feng et al. (Soft Compt 14:899–911, 2009 [4]) introduced lower and upper soft rough approximation of fuzzy sets in a soft approximation space and obtained a new hybrid model called soft rough fuzzy sets which is the extension of Dubois and Prade’s rough fuzzy sets. The aim of this chapter is to consider lower and upper soft rough intuitionistic fuzzy approximation of intuitionistic fuzzy sets in intuitionistic fuzzy soft approximation space (IF soft approximation space) and obtain a new hybrid model called soft rough intuitionistic fuzzy sets which can be seen as extension of both the previous work by Dubois and Prade and Feng et al.

A new fuzzy connective and network structure for queries which are constructed by fuzzy connectives are proposed to overcome a drawback of conventional fuzzy retrieval systems. In a conventional fuzzy retrieval system, it is quite difficult for a user to obtain the most suitable results since the user cannot start with making up complete queries. In our retrieval system, if a user gives an estimation of fitting samples in a database which fit the user's requests, AND/OR operators in queries which are made up of fuzzy connectives are adjusted to represent the user's requests. With the adjusted parameters and a network for the query, this fuzzy retrieval system gives results which better satisfy the user's requests. The consistencies of samples are also discussed. Inconsistent samples are defined, and an extracting method for inconsistent samples is proposed. The effectiveness of this proposed fuzzy retrieval system is shown through an experiment. >

In recent years, the availability of complex data repositories (e.g., multimedia, genomic, semistructured databases) has paved the way to new potentials as to data querying. In this scenario, similarity and fuzzy techniques have proven to be successful principles for effective data retrieval. However, most proposals are domain specific and lack of a general and integrated approach to deal with generalized complex queries, i.e., queries where multiple conditions are expressed, possibly on complex as well as on traditional data. To overcome such limitations, much work has been devoted to the development of middleware systems to support query processing on multiple repositories. On a similar line, We present a formal framework to permeate complex similarity and fuzzy queries within a relational database system. As an example, we focus on multimedia data, which is represented in an integrated view with common database data. We have designed an application layer that relies on an algebraic query language, extended with MM-tailored operators, and that maps complex similarity and fuzzy queries to standard SQL statements that can be processed by a relational database system, exploiting standard facilities of modern extensible RDBMS. To show the applicability of our proposal, we implemented a prototype that provides the user with rich query capabilities, ranging from traditional database queries to complex queries gathering a mixture of Boolean, similarity, and fuzzy predicates on the data.

In this paper, we propose a concept on fuzzy soft set with negative hesitation degree. Considering the unclear information on fuzzy sets, it is represented as the overlap with the intersection of known sets over the universe of discourse. The hesitation part is clarified with detail by analyzing the overlap area belonging to the intersection of known sets or not involving in any known sets. It also resolves the limitations in intuitionistic fuzzy sets and Pythagorean fuzzy sets. With the combination of membership degree and non-membership degree, (negative)hesitation is defined in their domain. From the definition, it is more flexible for fuzzy sets characterizing when the negative hesitation degree used to describe information.

According to the results of the analyss of the latest researches and publications of the existing methods of modeling the energy efficiency of the enclosing structures of buildings, a hypothesis is defined, which is based on the assumption of the need and possibility of improving the energy performance of the thermal insulation shell. The further development of the toolkit for evaluating organizational and technological solutions to ensure optimal energy efficiency of the thermal insulation shell involves taking into account the systemic impact of determining factors to minimize energy costs for installation, operation and recycling of building envelope construction materials. Taking into account that expert data on the parameters of influence on the energy efficiency of fencing structures is characterized by quantitative and qualitative values, the theory of fuzzy sets and linguistic variables was used as a mathematical tool for modeling. The structure of a hierarchical neuron-fuzzy network is proposed, which corresponds to a tree of logical conclusion regarding the energy efficiency of fencing structures. A fuzzy matrix of knowledge about the relationship at the system level of the influence of determining factors on the energy efficiency of the enclosing structures of buildings has been developed. Factors influencing the energy efficiency of building envelope structures are considered as linguistic variables defined on the corresponding universal sets and evaluated by fuzzy terms, which are a linguistic variable.

Emerging trends in soft set theory and related topics.

Article Free Access Share on Processing queries for first-few answers Authors: Roberto J. Bayardo Department of Computer Sciences and Applied Research Laboratories, The University of Texas at Austin Department of Computer Sciences and Applied Research Laboratories, The University of Texas at AustinView Profile , Daniel P. Miranker Department of Computer Sciences and Applied Research Laboratories, The University of Texas at Austin Department of Computer Sciences and Applied Research Laboratories, The University of Texas at AustinView Profile Authors Info & Claims CIKM '96: Proceedings of the fifth international conference on Information and knowledge managementNovember 1996 Pages 45–52https://doi.org/10.1145/238355.238372Online:12 November 1996Publication History 24citation256DownloadsMetricsTotal Citations24Total Downloads256Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper proposes a fuzzy retrieval system for purchasing cars employing image processing. This system aims to support such persons who are not good with machines or cars. When they try to purchase a car, they can use this system easily as if they ask casually someone else who knows more about car. Unspecific conditions are expressed by the fuzzy set, and the level matching conditions are expressed by the grade values. To use this more practically, a GUI form with selection menus is developed. In addition, calculating curvature by the car shape using the image processing, and adding an item for selecting a car shape from roundness and sharpness, try to improve usability of this system.

Rough set theory for the interval-valued fuzzy information systems

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