Modeling linguistic qualifiers of uncertainty in a fuzzy database

In this contribution an interpretation of linguistic qualifiers of uncertainty is proposed in the context of fuzzy databases; these qualifiers are defined in the framework of possibility theory to express incomplete knowledge on the represented world. The uncertain and vague information represented in a fuzzy database is formalized based on the notion of prioritized constraints with safeguard. © 2000 John Wiley & Sons, Inc.     

An ACS-based framework for fuzzy data mining

Data mining is often used to find out interesting and meaningful patterns from huge databases. It may generate different kinds of knowledge such as classification rules, clusters, association rules, and among others. A lot of researches have been proposed about data mining and most of them focused on mining from binary-valued data. Fuzzy data mining was thus proposed to discover fuzzy knowledge from linguistic or quantitative data. Recently, ant colony systems (ACS) have been successfully applied to optimization problems. However, few works have been done on applying ACS to fuzzy data mining. This thesis thus attempts to propose an ACS-based framework for fuzzy data mining. In the framework, the membership functions are first encoded into binary-bits and then fed into the ACS to search for the optimal set of membership functions. The problem is then transformed into a multi-stage graph, with each route representing a possible set of membership functions. When the termination condition is reached, the best membership function set (with the highest fitness value) can then be used to mine fuzzy association rules from a database. At last, experiments are made to make a comparison with other approaches and show the performance of the proposed framework.     

Fuzzy rough set techniques for uncertainty processing in a relational database

This paper concerns the modeling of imprecision, vagueness, and uncertainty in databases through an extension of the relational model of data: the fuzzy rough relational database, an approach which uses both fuzzy set and rough set theories for knowledge representation of imprecise data in a relational database model. The fuzzy rough relational database is formally defined, along with a fuzzy rough relational algebra for querying. Comparisons of theoretical properties of operators in this model with those in the standard relational model are discussed. An example application is used to illustrate other aspects of this model, including a fuzzy entity–relationship type diagram for database design, a fuzzy rough data definition language, and an SQL‐like query language supportive of the fuzzy rough relational database model. This example also illustrates the ease of use of the fuzzy rough relational database, which often produces results that are better than those of conventional databases since it more accurately models the uncertainty of real‐world enterprises than do conventional databases through the use of indiscernibility and fuzzy membership values. ©2000 John Wiley & Sons, Inc.     

Algebraic operations in fuzzy object-oriented databases

The fuzzy object-oriented databases have been proposed to meet the need of dealing with fuzzy as well as complex objects. In this paper, we present a formal fuzzy object-oriented database model. Based on the semantic measure of fuzzy data, we first identify two kinds of fuzzy object redundancies, which are inclusion redundancy and equivalence redundancy, and then define three kinds of merging operation for redundancy removal. On the basis, we define some fuzzy algebraic operations for fuzzy classes and fuzzy objects. Finally, in the paper, we discuss fuzzy querying strategies and give the form of SQL-like fuzzy querying for the fuzzy object-oriented databases.     

Conceptual design of fuzzy object-oriented databases using extended entity-relationship model

Entity-relationship–extended entity-relationship models play a crucial role in the conceptual design of relational databases as well as object-oriented databases. Recently, several approaches have been proposed to enhance object-oriented databases (OODBs) using fuzzy set theory. In this paper, we introduce a fuzzy extended entity-relationship model to cope with imperfect as well as complex objects in the real world at a conceptual level. In particular, we provide the formal approach to mapping a fuzzy extended entity-relationship model to a fuzzy object-oriented database schema. © 2001 John Wiley & Sons, Inc.     

Rewriting rules to permeate complex similarity and fuzzy queries within a relational database system

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.     

Design and implementation of index structures for fuzzy spatial databases

Over the years database management systems have evolved to include spatially referenced data. Because spatial data are complex and have a number of unique constraints (i.e., spatial components and uncertain properties), spatial database systems can be effective only if the spatial data are properly handled at the physical level. Therefore, it is important to develop an effective spatial and aspatial indexing technique to facilitate flexible spatial and/or aspatial querying for such databases. For this purpose we introduce an indexing approach to use (fuzzy) spatial and (fuzzy) aspatial data. We use a number of spatial index structures, such as Multilevel Grid File (MLGF), G-tree, R-tree, and R^*-tree, for fuzzy spatial databases and compare the performances of these structures for various flexible queries. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 805–826, 2007.     

Fuzzy database query languages and their relational completenesstheorem

Two fuzzy database query languages are proposed. They are used to query fuzzy databases that are enhanced from relational databases in such a way that fuzzy sets are allowed in both attribute values and truth values. A fuzzy calculus query language is constructed based on the relational calculus, and a fuzzy algebra query language is also constructed based on the relational algebra. In addition, a fuzzy relational completeness theorem such that the languages have equivalent expressive power is proved     

基于XML异构数据库数据转换技术的研究

文章是对数据共享和同步技术的研究,主要是针对不同部门数据库之间的数据转换技术,采用系统服务组件的设计理念,耦合程度低。主要实现方式是采用XML文件格式来描述异构数据库的结构信息,然后采用模糊匹配的方式计算两个数据库的异构程度,抽象出符合目标数据库的表结构。最后按照这个结构抽取源数据库的有用数据,导入目标数据库中。     

Bipolar database querying using bipolar satisfaction degrees

When expressing their information needs in a (database) query, users sometimes prefer to state what has to be rejected rather than what has to be accepted. In general, what has to be rejected is not necessarily the complement of what has to be accepted. This phenomenon is commonly known as the heterogeneous bipolar nature of expressing information needs. Satisfaction degrees in regular fuzzy querying approaches are based on the “symmetric'' assumption that the extent to which a database record, respectively, satisfies and does not satisfy a given query are complements of each other and are therefore less suited to adequately handle heterogeneous bipolarity in query specifications and query processing. In this paper, we present a bipolar query satisfaction modeling framework which is based on pairs that consist of an independent degree of satisfaction and degree of dissatisfaction. The use and advantages of the framework are illustrated in the context of fuzzy query evaluation in regular relational databases. More specifically, the evaluation of heterogeneous bipolar queries that contain both positive, negative, and bipolar criteria is studied. © 2011 Wiley Periodicals, Inc.     

On the primitivity of the division of fuzzy relations

 Allowing for flexible queries enables database users to express preferences inside elementary conditions and priorities between conditions. The division is one of the algebraic operators defined in order to query regular databases. This operation aims at the selection of A-elements which are connected with (at least) a given subset of B-elements, e.g., the stores which ordered all the items supplied by a given manufacturer. It is mainly used in the framework of the relational model of data, although it makes sense in object-oriented databases as well. In the relational context, the division is a non-primitive operation which may be expressed in terms of other operations, namely projection, Cartesian product and set difference. When fuzzy predicates appear, this operator needs to be extended to fuzzy relations and this requires the replacement of the usual implication by a fuzzy one. This paper proposes two types of meaning of the extended division and it investigates the issue of the primitivity of the extended operation (i.e., if the division of fuzzy relations is expressible in terms of other operations). The final objective is to decide whether this operator is necessary or not for the purpose of flexible querying and to help the design of a query language supporting flexible queries, among which those conveying a division of fuzzy relations.     

A fuzzy reasoning database question answering system

Describes a question-answering system based on fuzzy logic. The proposed system provides the capability to assess whether a database contains information pertinent to a subject of interest by evaluating each comment in the database via a fuzzy evaluator that attributes a fuzzy membership value indicating its relationship to the subject. An assessment is provided for the database as a whole regarding its pertinence to the subject of interest, and consequently comments that are considered irrelevant to the subject may be discarded. The system has been developed for the examination of databases that were created during the development of the IBM 4381 computer systems, for bookkeeping purposes, to assess whether such databases contain information pertinent to the functional changes that occurred during the development cycle. The system, however, can be applied with minimal changes to a variety of circumstances, provided that the fundamental assumptions for the development of the membership functions are respected in the new application. Its applicability, without modifications, assuming the same subject of interest, is granted for databases comprising similar characteristics to that of the original database for which the system has been developed     

Iris quality assessment and bi-orthogonal wavelet based encoding for recognition

Iris recognition has been demonstrated to be an efficient technology for personal identification. In this work, methods to perform iris encoding using bi-orthogonal wavelets and directional bi-orthogonal filters are proposed and compared. All the iris images are enhanced using the wavelet domain in-band de-noising method. This method is shown to improve the iris segmentation results. A framework to assess the iris image quality based on occlusion, contrast, focus and angular deformation is introduced and used as part of a novel adaptive matching technique based on the assessed iris image quality. Adaptive matching presents improved performance when compared against the Hamming distance method. Four different databases are used to analyze the system performance. The first two databases include popular CASIA and high resolution University of Bath databases. Results obtained for these databases compare with results from the literature, in terms of speed as well as accuracy. The other two databases have challenging off-angle (WVU database) and uncontrolled (Clarkson database) iris images and are used to assess the limits of system performance. Best results are achieved for directional bi-orthogonal filter based encoding technique combined with the adaptive matching method with EER values of 0.07%, 0.15%, 0.81% and 1.29% for the four databases, which reflect highly competent performance and high correlation with the quality of the iris images.     

Implementation of fuzzy classification in relational databases using conventional SQL querying

In this paper, a framework for implementing fuzzy classifications in information systems using conventional SQL querying is presented. The fuzzy classification and use of conventional SQL queries provide easy-to-use functionality for data extraction similar to the conventional non-fuzzy classification and SQL querying. The developed framework can be used as data mining tool in large information systems and easily integrated with conventional relational databases. The benefits of using the presented approach include more flexible data analysis and improvement of information presentation at the report generation phase. To confirm the theory, a prototype was developed based on the stored procedures and database extensions of Microsoft SQL Server 2000.     

基于RDBMS的XML数据管理技术研究

XML是一种专门为Internet所设计的标记语言,但是它已逐渐成为Internet上数据表示以及数据交换的标准,是一种发展势头良好的新兴数据管理手段.关系数据库管理系统(RDBMS)是一种技术成熟、应用十分广泛的系统.在数据管理上,XML技术和数据库技术各有优势和不足,XML和数据库结合技术成为学术界的研究热点.在对XML和数据库结合技术进行了研究的基础上一个基于RDBMS的XML数据管理的实现框架(XRM)被提出,该框架依据不同的映射策略,解析Schema文件或DTD,生成对应的关系模式,利用RDBMS存储中间件,使用户能透明地通过RDBMS来管理XML数据.该框架充分考虑了结构的灵活性和扩展性.     

Fuzzy relations and fuzzy relational databases

This paper deals with the connections existing between fuzzy set theory and fuzzy relational databases. Our new result dealing with fuzzy relations is how to calculate the greatest lower bound (glb) of two similarity relations. Our main contributions in fuzzy relational databases are establishing from fuzzy set theory what a fuzzy relational database should be (the result is both surprising and elegant), and making fuzzy relational databases even more robust.Our work in fuzzy relations and in fuzzy databases had led us into other interesting problems—two of which we mention in this paper. The first is primarily mathematical, and the second provides yet another connection between fuzzy set theory and artificial intelligence. In understanding similarity relations in terms of other fuzzy relations and in making fuzzy databases more robust, we work with closure and interior operators; we present some important properties of these operators. In establishing the connection between fuzzy set theory and artificial intelligence, we show that an abstraction on a set is in fact a partition on the set; that is, an abstraction defines an equivalence relation on the underlying set.     

Anytime mining for multiuser applications

Database systems have been designed to serve multi-users in real-world applications. There are essential differences between mono- and multi-user applications when a database is very large. Therefore, this paper presents an "anytime" framework for mining very large databases which are shared by multi-users. Anytime mining is designed to generate approximate results such that these results can be accessed at any time while the system is autonomously mining a database.     

基于Web Services和Hibernate技术的数据库中间件

为了能够使不同网络中异构数据库的分布式数据管理更加灵活和简便,本文研究和分析常用的数据库中间件技术以及目前流行的开源ORM框架Hibernate和Web Service技术。针对目前数据库中间件不足之处,提出利用Web Service技术和Hibernate框架设计一种新的数据库访问中间件。该方案能在不改变原始数据的存储和管理方式下,较好地实现不同网络中异构数据源统一透明访问,保证数据完整性、一致性和安全性,具有较高的开发效率。