基于相似度的粗关系数据库的近似查询

基于数据库理论和粗集方法研究了粗关系数据库中不确定数据的存储、索引和检索。提出了分别采用邻接表和十字链表实现粗关系数据库中属性值等价类和元组数据的存储;借助汉明距离和聚类方法,提出了实现粗关系数据库索引的方法;提出一种基于Rough集中的上、下近似计算数据间的相似度,并基于相似度给出了对粗关系数据库进行查询的模型,设计了相应的查询算法。最后,通过一个具体实例说明了查询算法的可行性和有效性。     

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.     

语义Web粗糙本体：定义、模型和存储方法

在讨论语义Web粗糙本体定义、模型的基础之上,鉴于关系数据库是目前最通用的存储介质,提出了基于关系数据库的语义Web粗糙本体存储方法。重点讨论了语义Web粗糙本体的存储模式,并通过构建一个粗糙本体并其存储到关系数据库中。实例验证了所提出存储方法的有效性和可行性。     

基于相似度对Rough关系数据库检索

Rough关系数据库模型(rough relational database model,RRDM)是对关系数据库模型的一种扩展,它具有在很大程度上比较符合真实世界情况的特性.介绍了Rough集和Rough关系数据库(rough relational database,RRDB)等相关概念,着重讨论如何在RRDB中通过Rough集中的上、下近似计算数据间的相似度来检索出需要的数据,给出了相关算法和实例分析.     

面向ROLAP的时态垂直粗分区方法

数据仓库为海量数据上的决策支持提供了一个高效的信息管理平台,ROLAP利用关系型数据仓库操纵灵活和技术成熟等优势,为面向数据仓库的分析和决策提供了有效的存取、建模和操作方法.然而,传统关系存取方法造成ROLAP的I/O有效性面临严峻的挑战.首先通过分析DSS应用的特点,提出了关系算子访问基表属性的时态行为,定义了算子对属性的时态局部访问.通过对查询样本集的解析建立算子与属性的时态访问映射矩阵,将有效增益作为属性的聚类准则得到时态访问模型PD.最后,给出了求解该模型的粗集算法以及依据聚类结果设计的基表属性的垂直分区方案.实验证明:在决策支持应用中,该方法的效率优于同类的其它优化分区方法.     

粗关系数据库中的数据更新

根据粗关系数据库中数据的特性,借助邻接表、十字链表存储不确定性数据,其中邻接表用于等价类的存储,十字链表用于数据库中基本表的存储。与传统的关系数据库更新不同,在粗关系数据库中更新基本表时,相应地等价类也要随之更新,该存储结构加快了对数据库中的数据更新速度。将算法与实例相结合,根据用户条件详细地讨论对等价类和RRDB中基本表的数据更新。     

基于粗集的模糊属性值信息系统的知识获取

文章研究了一类属性取模糊值的不完备信息系统的知识获取方法。首先给出了信息系统的一种表示方法,同时采用模糊集思想,构造了这类系统的模糊不可分辨关系,并研究了这类信息系统的知识获取算法,并以一个实例,说明了这种算法的有效性。     

粗糙模糊集的构造与公理化方法

用构造性方法和公理化研究了粗糙模糊集．由一个一般的二元经典关系出发构造性地定义了一对对偶的粗糙模糊近似算子，讨论了粗糙模糊近似算子的性质，并且由各种类型的二元关系通过构造得到了各种类型的粗糙模糊集代数．在公理化方法中，用公理形式定义了粗糙模糊近似算子，各种类型的粗糙模糊集代数可以被各种不同的公理集所刻画．阐明了近似算子的公理集可以保证找到相应的二元经典关系，使得由关系通过构造性方法定义的粗糙模糊近似算子恰好就是用公理化定义的近似算子。     

粗糙关系数据库空间结构及其粗糙集模型

粗糙关系数据库模型从本质上来说就是多值信息系统,它继承和扩展了经典的粗糙集理论,但与经典粗糙集理论又有着很大的不同。该文扩展了多值信息系统模型的定义,分析了粗糙关系数据库模型的空间结构,为粗糙数据库的不确定性进行度量提供一个依据,同时通过对粗糙关系数据库空间结构的分析,导出了基于粗糙关系数据库的上、下近似以及值约简,进一步基于粗糙关系数据库的空间结构定义了属性集之间的部分、传递依赖关系和粗糙关系数据库的规范,定义粗糙关系数据库的连接算子,并以此为基础得出了粗糙关系数据库的关系模式分解定理,为粗糙关系数据库模型的进一步研究提供了理论基础。     

基于相容粗糙集的图形图像信息预检索

早期的利用粗糙集理论进行信息检索都是以“等价粗糙集模型”为基础的，但是等价粗糙集的性质限制了该方法的应用范围，为此有些研究者提出以“相容粗糙集模型”代替“等价粗糙集模型”的新的信息检索的概念，此概念的关键在于“关键词的同时发生”和关于相容粗包含的“匹配算法”，提出了一种利用“相容粗糙集”的理论对图形图像进行预检索的新方法，即在相容类的近似空间里对图形图像进行预检索。为了验证这种新方法的有效性，在人脸图形和图像库中做了若干实验，实验结果表明，该方法可以有效地克服等价粗糙集在图形图像检索方面的限制，对提高图形图像的检索效率具有一定的作用。     

Representation of spatial data in an OODB using roughand fuzzy set modeling

This paper introduces and formally defines a fuzzy rough object-oriented database (OODB) model based on a formal framework using an algebraic type system and formally defined constraints. This generalized model incorporates both rough set and fuzzy set uncertainty, while remaining compliant with object-oriented database standards set forth by the Object Database Management Group. Rough and fuzzy set uncertainty enhance the OODB model so that it can more accurately model real world applications. Spatial databases have a particular need for uncertainty management that can be achieved through rough and fuzzy techniques.     

粗集中不可分辨类算法求解实现

从原始信息系统出发,用不可分辨类的思想求解系统的核属性和约简是粗集理论的精华。因此,如何在计算机上实现这种思想关系到读者对粗集理论抽象概念的理解。文中利用MATLAB语言编程实现了不可分辨类算法。考虑该语言的特点,首先将原始信息系统转换为数字型矩阵,然后自定义用户函数attvalist,并结合MATLAB语言中丰富的矩阵与向量运算函数,不经复杂的编程完成了样本分类。     

一种基于粗糙集的模糊数学形态学方法

粗糙集理论是一种新的处理含糊和不确定性问题的教学工具。本文基于粗糙集和数学形态学算子之间的联系和粗糙集中模糊度的概念，将结构元素作为不可分辨关系，提出了一种新的基于粗糙集的模糊数学形态学方法。该方法无需任何经验知识，直接从图像数据中获得所需隶属函数。最后，将其用于图象的滤波，获得了很好的实验结果。     

Data analysis based on discernibility and indiscernibility

Rough set theory models similarities and differences of objects based on the notions of indiscernibility and discernibility. With respect to any subset of attributes, one can define two pairs of dual relations: the strong indiscernibility and weak discernibility relations, and the weak indiscernibility and strong discernibility relations. The similarities of objects are examined by the indiscernibility relations, and the differences by the discernibility relations, respectively. Alternatively, one can construct an indiscernibility matrix to represent the family of strong indiscernibility or weak discernibility relations. One also can construct a discernibility matrix to represent the family of strong discernibility or weak indiscernibility relations. The consideration of the matrix-counterpart of relations, and the relation-counterpart of matrices, brings more insights into rough set theory.Based on indiscernibility and discernibility, three different types of reducts can be constructed, keeping the indiscernibility, discernibility, and indiscernibility-and-discernibility relations, respectively. Although the indiscernibility reducts have been intensively studied in the literature, the other two types of reducts are relatively new and require more attention. The existing methods for constructing the indiscernibility reducts also can be applied to construct the other two types of reducts. An empirical experiment for letter recognition is reported for demonstrating the usefulness of the discussed relations and reducts.     

Incomplete Information Tables and Rough Classification

The rough set theory, based on the original definition of the indiscernibility relation, is not useful for analysing incomplete information tables where some values of attributes are unknown. In this paper we distinguish two different semantics for incomplete information: the "missing value" semantics and the "absent value" semantics. The already known approaches, e.g. based on the tolerance relations, deal with the missing value case. We introduce two generalisations of the rough sets theory to handle these situations. The first generalisation introduces the use of a non symmetric similarity relation in order to formalise the idea of absent value semantics. The second proposal is based on the use of valued tolerance relations. A logical analysis and the computational experiments show that for the valued tolerance approach it is possible to obtain more informative approximations and decision rules than using the approach based on the simple tolerance relation.     

基于Rough表达的时态关系代数

现实世界的信息是和时间紧密相关的,因而记录和处理信息的数据库系统应该对时态问题进行考虑,以便更有效地解决现实问题;另一方面,现实世界的某些属性又很难用属性值简单描述。首先是用Rough理论对属性值进行一种刻画,用二元数组来表达属性值,并且将这种属性值的表达方式推广到一般情形,而后从时态映射的观点描述时态元组和时态关系,并由此形式描述相应的时态关系运算,从而建立了一套时态关系代数体系。     

一种基于粗糙集的信息系统决策规则提取方法

以粗糙集理论为基础,引入相似性的概念,并提出其衡量方法,改进了粗糙集理论中不可辨关系的确定条件,给出了基于新的相似关系的上下近似空间定义,并举例说明了基于粗糙集的相似性规则提取方法。     

一种基于语义的模糊关系数据库除操作

模糊信息的描述和处理只有符合自然语义才能反映事物本来面目，本文分析了传统关系数据库除操作，讨论了模糊关系数据库除操作的一般扩展形式和基于语义的运算方法－NQ扩展和运算，提出了除操作运算结果的个体活动满意度和团队活动满意度两种查询语义，并在此基础上给出了一种新的基于语义的模糊关系数据库除操作扩展和运算方法－PAT扩展和运算，通过实验测试和对比，结果表明PAT方法能使模糊信息的描述更逼近自然语义。     

Mathematical models of uncertainty with a regard to membrane systems

A brief review is presented of the known mathematical models of uncertainty taking into account its grounds such as randomness, indiscernibility, andvagueness. Then, one discusses the models of the uncertainty caused byindiscernibility and random indiscernibility with a regard to membrane systems.The discussed models include rough sets, probabilistic rough sets, andprobabilistic fuzzy sets. An algebraic characterization of P systems ispresented, which makes possible to ``transfer' the methods of Petri net theoryto P system theory including the approach of the first theory to models ofuncertainty.     

基于关系数据库的本体构建方法研究

本体可以很好地解决异构数据库集成中语义异构问题。由关系数据库模式转换到本体结构是信息集成的基础技术之一。该文提出了一种基于关系数据库的本体建模方法,并对构建过程中丢失的外键所包含的语义信息进行恢复,使得数据库的语义表达更为完整。