Semantic measure of fuzzy data in extended possibility‐based fuzzy relational databases

In this paper, we propose notions of equivalence and inclusion of fuzzy data in relational databases for measuring their semantic relationship. The fuzziness of data appears in attribute values in forms of possibility distribution as well as resemblance relations in attribute domain elements. An approach for evaluating semantic measures is presented. With the proposal, one can remove fuzzy data redundancy and define fuzzy functional dependency. © 2000 John Wiley & Sons, Inc.     

Handling fuzzy information in extended possibility‐based fuzzy relational databases

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: One is that attribute values are possibility distributions, and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility‐based fuzzy relational databases. In this paper, we focus on such fuzzy relational databases. We classify two kinds of fuzzy data redundancies and define their removal. On this basis, we define fuzzy relational operations in relational algebra, which, being similar to the conventional relational databases, are complete and sound. In particular, we investigate fuzzy querying strategies and give the form of fuzzy querying with SQL. © 2002 Wiley Periodicals, Inc.     

Updating extended possibility‐based fuzzy relational databases

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: one is that attribute values are possibility distributions and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility‐based fuzzy relational databases. In this article, we focus on such fuzzy relational databases and investigate three update operations for the fuzzy relational databases, which are Insertion, Deletion, and Modification, respectively. We develop the strategies and implementation algorithms of these operations. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 237–258, 2007.     

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.     

基于本体的关系数据集成的查询处理

为实现异构关系数据库的语义集成,针对传统集成技术存在的问题,在对语义网等相关技术进行分析的基础上,研究基于本体的关系数据集成系统中的查询处理问题,提出了一种基于本体的关系数据库集成框架。设计了基于本体的关系数据的描述方法,使用本体作为集成的全局模式来描述关系模式的语义。设计了查询重写算法,该算法可以将基于全局模式的SPARQL查询重写为针对具体关系数据库的查询,从而实现对异构关系数据库的集成。实验表明,该算法具有良好的可扩展性。     

Data dependencies in extended possibility‐based fuzzy relational databases

Based on the semantic equivalence degree the formal definitions of fuzzy functional dependencies (FFDs) and fuzzy multivalued dependencies (FMVDs) are first introduced to the fuzzy relational databases, where fuzziness of data appears in attribute values in the form of possibility attributions, as well as resemblance relations in attribute domain elements, called extended possibility‐based fuzzy relational databases. A set of inference rules for FFDs and FMVDs is then proposed. It is shown that FFDs and FMVDs are consistent and the inference rules are sound and complete, just as Armstrong's axioms for classic cases. © 2002 Wiley Periodicals, Inc.     

模糊关系数据库中的非对称冗余元组

在现实世界中,有些对象比其它的更具有一般性,两个对象的相似度可能不对称。两个对象之间的相似关系可能既不对称又不传递,我们用弱相似关系来表示。本文提出了非对称冗余元组来处理模糊关系数据库中的弱相似关系。非对称冗余元组的概念是模糊关系数据库的冗余概念的推广,它用来删除一些冗余信息,表示更精确的信息。     

Consistent data operations for multi-databases in extended possibility-based data models

This paper considers the data operation for multi-databases in an extended possibility-based data model. Owing to the complexity of the data model considered, inconsistent redundancy of tuples may occur when database relations being operated are associated with different resemblance relations on a given domain. This work first demonstrates the inconsistency problem, and then presents the notions of consistency constraints for multi-database design. Under the constraints, the extended possibility-based databases using different resemblance relations can preserve consistent redundancy.     

一种利用模糊关系数据库构建模糊OWL本体的方法

经典OWL本体不能直接表示和处理语义Web应用中广泛存在的模糊知识,鉴于模糊关系数据库在模糊数据表示与处理方面的优势,提出利用模糊关系数据库来构建模糊OWL本体.通过对RDF数据类型进行模糊扩展,并从模糊数据类型角度扩展OWL,解决了模糊OWL本体的表示问题;在此基础上,研究了以模糊关系数据库为数据源的模糊OWL本体的构建方法,该方法为语义Web中模糊本体的构建提供了一个有效的解决方案.     

支持起源回溯的最小化标识属性传播

数据起源主要描述数据的来源及随时间演化的过程。最小化查询数据表的标识属性传播是一个亟待解决的问题。通过构建等值传播链表EPL描述查询中的等值连接及其传递性,并基于EPL给出朴素标识属性传播方法实现高效溯源信息传播。然而标识属性通过等值连接可以识别非标识属性数值,简单地传播数据表的标识属性数值导致起源数据冗余传播。为避免溯源信息冗余,提出完全标识属性传播格及其剪枝策略,给出基于格剪枝的最优标识属性传播方法,实现溯源信息的最小代价传播。基于TPC-H Benchmark和人造数据集IAP-DB的实验结果验证了提出的基于标识属性传播的溯源方法可以高效实现数据起源信息传播。     

Storing and querying fuzzy XML data in relational databases

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy data management has been extensively investigated in various database management systems. Currently, introducing native support for XML data in relational database management systems (RDBMs) has attracted considerable interest with a view to leveraging the powerful and reliable data management services provided by RDBMs. Although there is a rich literature on XML-to-relational storage, none of the existing solutions satisfactorily addresses the problem of storing fuzzy XML data in RDBMs. In this paper, we study the methodology of storing and querying fuzzy XML data in relational databases. In particular, we present an edge-based approach to shred fuzzy XML data into relational data. The unique feature of our approach is that no schema information is required for our data storage. On this basis, we present a generic approach to translate path expression queries into SQL for processing XML queries.     

基于Vague关系数据模型的除操作研究

基于Vague集理论的Vague关系数据库由于其所含信息的模糊性,对应于现实世界的多种状态。为此,基于扩展的Vague关系数据模型,研究一种条件受限的Vague除操作实现方法,并给出相应的操作步骤和算法。实例分析结果表明,该实现方法可直接作用于整个Vague关系数据库,无需对Vague关系数据库对应的所有状态逐一进行扫描即可获得正确的操作结果,具有较高的执行效率。     

Reasoning of fuzzy relational databases with fuzzy ontologies

A significant interest developed regarding the problem of describing databases with expressive knowledge representation techniques in recent years, so that database reasoning may be handled intelligently. Therefore, it is possible and meaningful to investigate how to reason on fuzzy relational databases (FRDBs) with fuzzy ontologies. In this paper, we first propose a formal approach and an automated tool for constructing fuzzy ontologies from FRDBs, and then we study how to reason on FRDBs with constructed fuzzy ontologies. First, we give their respective formal definitions of FRDBs and fuzzy Web Ontology Language (OWL) ontologies. On the basis of this, we propose a formal approach that can directly transform an FRDB (including its schema and data information) into a fuzzy OWL ontology (consisting of the fuzzy ontology structure and instance). Furthermore, following the proposed approach, we implement a prototype construction tool called FRDB2FOnto. Finally, based on the constructed fuzzy OWL ontologies, we investigate how to reason on FRDBs (e.g., consistency, satisfiability, subsumption, and redundancy) through the reasoning mechanism of fuzzy OWL ontologies, so that the reasoning of FRDBs may be done automatically by means of the existing fuzzy ontology reasoner.© 2012 Wiley Periodicals, Inc.     

A Fuzzy Temporal Object‐Relational Database: Model and Implementation

In real world, some data have a specific temporal validity that must be appropiately managed. To deal with this kind of data, several proposals of temporal databases have been introduced. Moreover, time can also be affected by imprecision, vagueness, and/or uncertainty, since human beings manage time using temporal indications and temporal notions, which may also be imprecise. For this reason, information systems require appropriate support to accomplish this task. In this work, we present a novel possibilistic valid time model for fuzzy databases including the data structures, the integrity constraints, and the DML. Together with this model, we also present its implementation by means of a fuzzy valid time support module on top of a fuzzy object‐relational database system. The integration of these modules allows to perform queries that combines fuzzy valid time constraints together with fuzzy predicates. Besides, the model and implementation proposed support the crisp valid time model as a particular case of the fuzzy valid time support provided.     

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.     

XML函数依赖研究

为了消除数据冗余，基于关系数据模型的函数依赖理论已经被广泛接受并应用于关系数据库的设计中。XML数据库中同样存在数据冗余。为了设计没有冗余的XML数据库，需要用一种简洁、易于理解的方法来定义XML数据问依赖关系。在无序的结点标记树数据模型上定义了XML子树间的函数依赖，并且给出了一套公理系统用来推导函数依赖，以解决函数依赖的蕴涵问题，最后证明了该公理系统是合理的。     

基于关系数据库的模糊本体的存储方法

模糊本体被广泛用来描述语义Web上的模糊信息。如何对模糊本体进行有效的存储,逐渐成为语义Web领域一个重要的研究课题。鉴于关系数据库在数据组织与管理方面的优势,提出基于关系数据库的模糊本体的存储方法。首先,通过对RDF数据类型进行模糊扩展,给出模糊本体中模糊数据类型的表示方法;然后,提出了基于关系数据库的模糊本体的存储模式以及模糊本体的结构和实例在关系数据库中的存储方法;最后,证明了该存储方法的正确性。基于关系数据库的模糊本体的存储方法为语义Web中模糊数据的存储和管理提供了一个有效的解决方案。