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。     

Fuzzy relational algebra for possibility-distribution-fuzzy-relational model of fuzzy data

In the real world, there exist a lot of fuzzy data which cannot or need not be precisely defined. We distinguish two types of fuzziness: one in an attribute value itself and the other in an association of them. For such fuzzy data, we propose a possibility-distribution-fuzzy-relational model, in which fuzzy data are represented by fuzzy relations whose grades of membership and attribute values are possibility distributions. In this model, the former fuzziness is represented by a possibility distribution and the latter by a grade of membership. Relational algebra for the ordinary relational database as defined by Codd includes the traditional set operations and the special relational operations. These operations are classified into the primitive operations, namely, union, difference, extended Cartesian product, selection and projection, and the additional operations, namely, intersection, join, and division. We define the relational algebra for the possibility-distribution-fuzzy-relational model of fuzzy databases.     

A functional processor for the relational algebra on a microcomputer

We have built on the U.C.S.D. P-system (running on an IBM Personal Computer) a relational algebra processor, MRDS/FS, which is extremely powerful and which supports a functional syntax for the programmer-user. The relational algebra is provided in the extended operators μ-join, σ-join, project and select. The domain algebra is fully implemented for the first time, giving operations on attributes: arithmetic, logic, comparison and four different categories of aggregation of these. A strictly functional syntax is provided, permitting user-defined functions using the relational and domain algebras as primitive operations. An interactive editor permits the creation, copying and changing of both relations and user-defined functions.     

Temporal relational data model

This paper incorporates a temporal dimension to nested relations. It combines research in temporal databases and nested relations for managing the temporal data in nontraditional database applications. A temporal data value is represented as a temporal atom; a temporal atom consists of two parts: a temporal set and a value. The temporal atom asserts that the value is valid over the time duration represented by its temporal set. The data model allows relations with arbitrary levels of nesting and can represent the histories of objects and their relationships. Temporal relational algebra and calculus languages are formulated and their equivalence is proved. Temporal relational algebra includes operations to manipulate temporal data and to restructure nested temporal relations. Additionally, we define operations to generate a power set of a relation, a set membership test, and a set inclusion test, which are all derived from the other operations of temporal relational algebra. To obtain a concise representation of temporal data (temporal reduction), collapsed versions of the set-theoretic operations are defined. Procedures to express collapsed operations by the regular operations of temporal relational algebra are included. The paper also develops procedures to completely flatten a nested temporal relation into an equivalent 1 NF relation and back to its original form, thus providing a basis for the semantics of the collapsed operations by the traditional operations on 1 NF relations     

基于关系数据库的脆弱性水印算法研究

为了检测对关系数据库的恶意篡改,提出了一种脆弱性数字水印算法。该算法将数据库的元组划分到不同的分组中,在对每个分组内的元组进行秘密排序的基础上,生成由属性水印和元组水印构成的分组水印矩阵,因此可以将对数据库的篡改定位在分组范围内。利用单向哈希函数及关系数据动态生成水印,不但保证了水印信息的安全性,而且也实现了水印的盲检测。理论分析和实验结果表明,该方法能够有效探测攻击者对关系数据库进行元组添加、属性值修改、元组删除和属性变化四类操作,从而为关系数据的真实性认证提供依据。     

Distribution-based aggregation for relational learning with identifier attributes

Identifier attributes—very high-dimensional categorical attributes such as particular product ids or people's names—rarely are incorporated in statistical modeling. However, they can play an important role in relational modeling: it may be informative to have communicated with a particular set of people or to have purchased a particular set of products. A key limitation of existing relational modeling techniques is how they aggregate bags (multisets) of values from related entities. The aggregations used by existing methods are simple summaries of the distributions of features of related entities: e.g., MEAN, MODE, SUM, or COUNT. This paper's main contribution is the introduction of aggregation operators that capture more information about the value distributions, by storing meta-data about value distributions and referencing this meta-data when aggregating—for example by computing class-conditional distributional distances. Such aggregations are particularly important for aggregating values from high-dimensional categorical attributes, for which the simple aggregates provide little information. In the first half of the paper we provide general guidelines for designing aggregation operators, introduce the new aggregators in the context of the relational learning system ACORA (Automated Construction of Relational Attributes), and provide theoretical justification. We also conjecture special properties of identifier attributes, e.g., they proxy for unobserved attributes and for information deeper in the relationship network. In the second half of the paper we provide extensive empirical evidence that the distribution-based aggregators indeed do facilitate modeling with high-dimensional categorical attributes, and in support of the aforementioned conjectures. Editors: Hendrik Blockeel, David Jensen and Stefan Kramer An erratum to this article is available at .     

A novel fuzzy classifier with Choquet integral-based grey relational analysis for pattern classification problems

This paper aims to propose a fuzzy classifier, which is a one-class-in-one-network structure consisting of multiple novel single-layer perceptrons. Since the output value of each single-layer perceptron can be interpreted as the overall grade of the relationship between the input pattern and one class, the degree of relationship between an attribute of the input pattern and that of this class can be taken into account by establishing a representative pattern for each class. A feature of this paper is that it employs the grey relational analysis to compute the grades of relationship for individual attributes. In particular, instead of using the sigmoid function as the activation function, a non-additive technique, the Choquet integral, is used as an activation function to synthesize the performance values, since an assumption of noninteraction among attributes may not be reasonable. Thus, a single-layer perceptron in the proposed structure performs the synthetic evaluation of the Choquet integral-based grey relational analysis for a pattern. Each connection weight is interpreted as a degree of importance of an attribute and can be determined by a genetic algorithm-based method. The experimental results further demonstrate that the test results of the proposed fuzzy classifier are better than or comparable to those of other fuzzy or non-fuzzy classification methods.     

面向多元组关系-集值数据的脱敏方法

对数据发布中传统方法脱敏多元组关系-集值数据可能导致信息泄露以及产生较高信息损失的问题进行研究,提出基于(K,L)-多样性模型的多元组关系-集值数据的脱敏方法PAHI.根据准标识符将多元组数据转换为单元组数据;用信息增益比优化分割方法,实现集值数据K-匿名;引入敏感度值建立集值指纹桶,采用敏感度距离优化剩余元组的处理,...     

Resolving database incompatibility: an approach to performingrelational operations over mismatched domains

A solution to the problem of supporting relational database operations despite domain mismatch is presented. Mismatched domains occur when information must be obtained from databases that were developed independently. Domain differences are resolved by mapping conflicting attributes to common domains by means of a mechanism of virtual attributes and then applying a set of extended relational operations to the resulting values. When one-one mappings cannot be established between domains, the values that result from attribute mappings may be partial. A set of extended relational operators that formalize operations over partial values and thus manipulate the incomplete information that results from resolving domain mismatch is defined     

A relational database for non-manipulative representation of solid objects

As the heart of any solid modelling system, much effort has been spent on formulating the data models which represent the shape of a polyhedral solid object within the computer in an accurate, unique and complete manner. Most of this effort has been devoted to make the models manipulateable in terms of computational efficiency, and preserve their semantic integrity. The price paid for achieving these goals is a proliferation of cross-reference data links which, coupled with multiple redundancy, renders the models inefficient for storage or transmission over communication channels. Moreover, the interrogation of these models by non-manipulative applications is usually unsupported functionally, requiring direct accesses to the data, which involves extensive pointer chasing and therefore high familiarity with the manipulative data structure.This paper presents an example relational model as a complementary logical scheme for viewing the shape database that facilitates compact storage and supports non-manipulative query operations through the projection, selection and join operators defined for the relational model, without requiring expert knowledge of the manipulative structure. The flexibility of the relational model, compared with that of the hierarchical, manipulative one, allows easy extensibility and the association of non-geometric attributes with each data item.     

A new method for computing fuzzy functional dependencies in relational database systems

In this paper, we present a new method for computing fuzzy functional dependencies between attributes in fuzzy relational database systems. The method is based on the use of fuzzy implications. A literature analysis has shown that there is no algorithm that would enable the identification of attribute relationships in fuzzy relational schemas. This fact was the motive for development a new methodology in the analysis of fuzzy functional dependencies over a given set of attributes. Solving this, not so new problem, is not only research challenge having theoretical importance, but it also has practical significance. Possible applications of the proposed methodology include GIS, data mining, information retrieval, reducing data redundancy in fuzzy relations through implementation of logical database model, estimation of missing values etc.     

The relational model with relation-valued attributes

Motivated by new applications of database systems we introduce relations which may have relation-valued attributes and propose a related algebra. Formal definitions for this extended relational model can be given by applying usual notions recursively. The main problem considered in this paper is the formal definition of an appropriate relational algebra for these non-first-normal-form relations. We allow the application of the basic relational operators to any relation-valued attribute within a relation. This leads to a (hierarchically) nested relational algebra.     

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.     

多传感器目标识别的改进灰关联度法

针对具有多个特征指标的多传感器目标识别问题,提出了一种新的融合方法。该方法利用置信距离改进了灰关联系数,通过求解各属性的置信距离的偏差平方之和最大的优化问题,获得属性的权重,从而给出目标识别方法。克服了特征权重选取的主观性,提高了目标识别结果的准确性和可信度。仿真实例验证了方法的有效性。     

An instance-based learning approach based on grey relational structure

In instance-based learning, the ‘nearness’ between two instances—used for pattern classification—is generally determined by some similarity functions, such as the Euclidean or Value Difference Metric (VDM). However, Euclidean-like similarity functions are normally only suitable for domains with numeric attributes. The VDM metrics are mainly applicable to domains with symbolic attributes, and their complexity increases with the number of classes in a specific application domain. This paper proposes an instance-based learning approach to alleviate these shortcomings. Grey relational analysis is used to precisely describe the entire relational structure of all instances in a specific domain. By using the grey relational structure, new instances can be classified with high accuracy. Moreover, the total number of classes in a specific domain does not affect the complexity of the proposed approach. Forty classification problems are used for performance comparison. Experimental results show that the proposed approach yields higher performance over other methods that adopt one of the above similarity functions or both. Meanwhile, the proposed method can yield higher performance, compared to some other classification algorithms. Chi-Chun Huang is currently Assistant Professor in the Department of Information Management at National Kaohsiung Marine University, Kaohsiung, Taiwan. He received the Ph.D. degree from the Department of Electronic Engineering at National Taiwan University of Science and Technology in 2003. His research includes intelligent Internet systems, grey theory, machine learning, neural networks and pattern recognition. Hahn-Ming Lee is currently Professor in the Department of Computer Science and Information Engineering at National Taiwan University of Science and Technology, Taipei, Taiwan. He received the B.S. degree and Ph.D. degree from the Department of Computer Science and Information Engineering at National Taiwan University in 1984 and 1991, respectively. His research interests include, intelligent Internet systems, fuzzy computing, neural networks and machine learning. He is a member of IEEE, TAAI, CFSA and IICM.     

The automatic generation of test data for a relational database

A structure for a relational database system is described which involves a new structure called a linkage. A linkage is a set of interdependent relations. Together with relations, tuples, attributes and attribute-values, this provides a hierarchy of structures within which one may specify and generate test data which are not only valid with respect to attribute domains, but preserve dependencies. The discussion refers to an experimental system QIKSYS in which these ideas have been implemented and some of the features of this system are described.     

基于BCDM的含有变量的双时态关系代数研究*

在BCDM(双时态概念数据模型)中,时间变元Now和UC的存在解决了时态记录的频繁更新问题;但是时态变元的存在又给数据库的操作带来某种不确定性甚至是错误,同时引起关系代数运算的歧义。基于BCDM,通过绑定时间变元,用时态映射的集合定义时态关系,由此建立了一种含有时间变元的双时态关系代数体系。该关系代数体系在双时态运算下是封闭的。     

Translating update operations from relational to object-oriented databases

In migrating a legacy relational database system to the object-oriented (OO) platform, when database migration completes, application modules are to be migrated, where embedded relational database operations are mapped into their OO correspondents. In this paper we study mapping relational update operations to their OO equivalents, which include UPDATE¹, INSERT and DELETE operations. Relational update operation translation from relational to OO faces the touchy problem of transformation from a value-based relationship model to a reference-based model and maintaining the relational integrity constraints. Moreover, with a relational database where inheritance is expressed as attribute value subset relationship, changing of some attribute values may lead to the change of the position of an object in the class inheritance hierarchy, which we call object migration. Considering all these aspects, algorithms are given mapping relational UPDATE, INSERT and DELETE operations to their OO correspondents. Our work emphasize in examining the differences in the representation of the source schema's semantics resulting from the translation process, as well as differences in the inherent semantics of the two models.     

基于关系代数的XML数据查询

XML渐已成为Web上数据表示和交换的通用语言。为了有效地利用大量的XML文档，需要研究XML数据查询技术。提出了基于关系代数的XML数据查询方法。XML子句可以用关系代数表达式表示，并通过关系代数操作得到查询结果。该方法为查询XML数据提供了一种简洁和有效的手段。     

Extending the relational model to deal with probabilistic data

According to the soundness and completeness of information in databases,the expressive form and the semantics of incomplete information are discussed in this paper.On the basis of the discussion,the current studies on incomplete data in relational databases are reviewed.In order to represent stochastic uncertainty in most general sense in the real world,probabilistic data are introduced into relational databases.An extended relational data model is presented to express and manipulate probabilistic data and the operations in relational algebra based on the extended model are defined in this paper.