糊糊空值环境下关系数据库的操作及查询处理

本文在经典关系数据库理论基础上引入模糊数学理论的基本思想和方法,对模糊空值环境下关系数据库的几种数学运算和基本关系操作进行了讨论,进而给出了查询处理中选择运算、差运算和连接运算的基本方法.     

基于空值环境下扩展关系模型的查询处理研究

由于空值的出现，使数据库更新操作更趋势复杂。为适应更新要求，建立了扩展关系模型。本文正是基于这种模型进行对查询处理的讨论，给出了相应的查询策略及实现过程。     

空值环境下关系数据库的更新Ⅱ：扩展关系模型及基本运算

本文从空值语义及更新操作的关系出发，提出了一种新的扩展关系模型，用以组织更新操作下的含有空值的关系数据库中的信息，同时，定义了这种模型下的基本关系代数运算，为实现空值环境下关系数据库的数据更新奠定了基础。     

关系数据库不可用空值的查询与处理

现有的基于关系数据模型的商业数据库采用空值对缺失信息进行建模与处理,然而,单一的空值解释无法体现空值本身的丰富语义。事实上,在相关研究中空值通常被解释为‘值未知’,‘值不可用’以及‘值不存在’等。文中主要研究不可用空值的查询与处理。通过仔细地观察和深刻地理解,分别在传统关系数据库查询和模糊数据库查询中讨论不同语义背景和查询条件下不可用空值的处理和分类。此外,还针对涉及不可用空值的传统关系数据库查询提出选择运算和差运算算法,这些算法使文中的研究更具实用性。     

空值环境下关系数据库的更新Ⅱ：更新处理的插入,删除和修改操作算法

本文的这部分是以扩展模型为基础，以五种运算为工具，对空值环境下关系数据库的更新处理策略做了深入研究，并给出了相应的算法及算法分析。     

基于空值修复的数据库一致性查询方法*

完整性约束常用来定义数据库的数据语义,违反约束的数据库实例为不一致数据库,返回含有不一致结果的查询称为不一致查询。一致性查询目的在于不修改数据库实例而从不一致数据库获取满足约束的查询结果,已有方法因其支持的约束类型有限或计算复杂度高而影响其应用范围。提出了一种基于空值修复的数据库一致性查询方法,首先将原始完整性约束转换为与查询相关的统一约束,然后根据统一约束对原SQL查询进行查询重写,重写后的查询将不一致属性值当做空值来处理以获得满足完整性约束的结果。系统实现与实验证明,该方法在多种完整性约束类型与SQL     

一种新的关系数据库查询优化方法

现代关系数据库查询优化器通常根据查询代价评估不同查询计划的执行效率,对查询计划中产生的中间结果集的错误预测是造成优化器效率低下的主要原因。为了解决这个问题,本文介绍一种新的SPS（Statistics Predict Set）查询优化方法。该方法能够有效地解决这方面的问题。     

含有空值关系数据库的查询处理

本文在深刻理解空值语义的基础上，给出一种处理占位型空值的方法，空值环境下关系数据库的查询策略，定义了含三种查询操作的关系代数最小完备集中的关系代数运算，并对查询计算的有效性和完备性进行了分析。     

脏数据库的查询方法研究

对于给定的约束,多个数据源分别是一致的,但是在它们集成时可能是脏的.已经存在的技术能够通过特别的方法识别出数据集成环境下的脏数据,但是不能进行有效处理.分析查询对应的连接图是否为有向连接图,判断查询是否可重写,并且给出了元组概率计算和基本查询重写方法.使用TPC-H基准的数据和查询比较脏数据多粒度的执行性能,实验显示方法是可行的.     

空值环境下含不确定及可能信息关系数据库的查询

本文以（１）中的扩展关系模型为基础在两种元组极的不完全信息－不确定及可能信息中引入属性级的不完全信息空值，使两种不同性质的不完全信息同时出现在同一关系中，为了能够查询到不同种类及不同确定程度的信息，文中制定了这种扩展关系模型上关系的查询策略，定义了能够体现这种策略的最小关系代数运算。     

ESTIMATING NULL VALUES IN THE DISTRIBUTED RELATIONAL DATABASES ENVIRONMENT

To estimate nullvalues in relational database systems is an important research topic. In Chen and Yeh (1997) a method for estimating null values in relational database systems was presented. In Chen and Chen (1997) a method for fuzzy query translation for information in the distributed relational databases environment was presented. In this article, the works of Chen and Chen (1997) and Chen and Yeh (1997) are extended to propose a method for estimating null values in the distributed relational databases environment. The proposed method provides a useful way to estimate incomplete data when the relations stored in a failed server cannot be accessed in the distributed relational databases environment.     

A NEW METHOD FOR FUZZY QUERY PROCESSING IN RELATIONAL DATABASE SYSTEMS

In this paper, we present a new method for fuzzy query processing in relational database systems based on automatic clustering techniques and weighting concepts. The proposed method allows the query conditions and the weights of query items of users' fuzzy SQL queries to be described by linguistic terms represented by fuzzy numbers. Because the proposed fuzzy query processing method allows the users to construct their fuzzy queries more conveniently, the existing relational database systems will be more intelligent and more flexible to the users.     

A NEW METHOD TO GENERATE FUZZY RULES FROM RELATIONAL DATABASE SYSTEMS FOR ESTIMATING NULL VALUES

Fuzzy decision trees can be used to generate fuzzy rules from training instances to deal with forecasting and classification problems. We propose a new method to construct fuzzy decision trees from relational database systems and to generate fuzzy rules from the constructed fuzzy decision trees for estimating null values, where the weights of attributes are used to derive the values of certainty factors of the generated fuzzy rules. We use the concept of "coefficient of determination" of the statistics to derive the weights of the attributes in relational database systems and use the normalized weights of the attributes to derive the values of certainty factors of the generated fuzzy rules. Furthermore, we also use regression equations of the statistics to construct a complete fuzzy decision tree for generating better fuzzy rules. The proposed method obtains a higher average estimated accuracy rate than the existing methods for estimating null values in relational database systems.     

含空值关系数据库中关系代数的实现

本文从空值的完全语义出发，依据空值环境下信息等价和信息相容的含义，全面定义了空值环境下运算结果完备的关系代数运算，并对空值环境下关系代数运算的有效性和完备性进行了讨论。     

关系数据库中的模糊知识发现

本文提出用部分量词和模糊谓词来表示模糊知识这一方法，在简要介绍数据抽象这一关系数据库中知识发现的方法后，详细描述了该方法中对模糊性问题的处理方法．     

一种基于两步状态法的外包关系数据库查询执行技术

针对基于属性分解的外包关系数据库方案中属性集分散存储造成的执行查询困难问题,在包含可信第三方的外包数据库服务架构下,提出一种基于两步状态法的多DSP协同的查询执行机制。该机制在保护用户查询内容隐私的前提下,通过对用户查询进行元语句划分、状态值计算等实现了基于属性分解的外包关系数据库服务方案对用户查询的支持。实例分析验证了该技术在用户查询分解和查询结果获取方面的正确性和有效性。     

OODBS中查询优化和查询处理技术

本文先扼要介绍了已成成功用于关系查询优化和查询处理的技术也可应用于ＯＯ查询优化和查询处理的理由。接着简要概括了这些技术，最后讨论了将这些技术用于ＯＯ查询优化和查询处理时应作的必要修改和扩充。     

LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH

Knowledge discovery in databases, or dala mining, is an important direction in the development of data and knowledge-based systems. Because of the huge amount of data stored in large numbers of existing databases, and because the amount of data generated in electronic forms is growing rapidly, it is necessary to develop efficient methods to extract knowledge from databases. An attribute-oriented rough set approach has been developed for knowledge discovery in databases. The method integrates machine-learning paradigm, especially learning-from-examples techniques, with rough set techniques. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of database learning processes. Then the cause-effect relationship among the attributes in the database is analyzed using rough set techniques, and the unimportant or irrelevant attributes are eliminated. Thus concise and strong rules with little or no redundant information can be learned efficiently. Our study shows that attribute-oriented induction combined with rough set theory provide an efficient and effective mechanism for knowledge discovery in database systems.     

GENERATING AUTOMATIC FUZZY SYSTEM FROM RELATIONAL DATABASE SYSTEM FOR ESTIMATING NULL VALUES

There are many methods trying to do relational database estimations with a highly estimated accuracy rate by constructing a fuzzy learning algorithm automatically. However, there exists a conflict between the degree of the interpretability and the accuracy of the approximation in a general fuzzy system. Thus, how to make the best compromise between the accuracy of the approximation and the degree of the interpretability is a significant study of the subject. In order to achieve the best compromise, this article attempts to propose a simple fuzzy learning algorithm to get a positive result in the relational database estimation on the real world database system, including partition determination, automatic membership function, and rule generation, and system approximation.