A new method to estimate null values in relational database systems based on automatic clustering techniques

In this paper, we present a new method for estimating null values in relational database systems based on automatic clustering techniques. The proposed method clusters data in advance, such that it only needs to process the most proper clusters instead of all the data in the relational database system for estimating null values. The average estimated accuracy rate of the proposed method is better than the existing methods for estimating null values in relational database systems.     

Estimating null values in relational database systems using automatic clustering and multiple regression techniques

In this paper, we present a new method for estimating null values in relational database systems using automatic clustering and multiple regression techniques. First, we present a new automatic clustering algorithm for clustering numerical data. The proposed automatic clustering algorithm does not need to determine the number of clusters in advance and does not need to sort the data in the database in advance. Then, based on the proposed automatic clustering algorithm and multiple regression techniques, we present a new method to estimate null values in relational database systems. The proposed method estimating null values in relational database systems only needs to process a particular cluster instead of the whole database. It gets a higher average estimation accuracy rate than the existing methods for estimating null values in relational database systems.     

GENERATING FUZZY RULES FROM RELATIONAL DATABASE SYSTEMS FOR ESTIMATING NULL VALUES

This paper presents a new algorithm for constructing fuzzy decision trees from relational database systems and generating fuzzy rules from the constructed fuzzy decision trees. We also present a method for dealing with the completeness of the constructed fuzzy decision trees. Based on the generated fuzzyrules, we also present a method for estimating null values in relational database systems. The proposed methods provide a useful way to estimate null values in relational database systems.     

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 approach to generate weighted fuzzy rules using genetic algorithms for estimating null values

In this paper, we present a new method to generate weighted fuzzy rules using genetic algorithms for estimating null values in relational database systems, where there are negative functional dependency relationships between attributes. The proposed method can get higher average estimated accuracy rates than the method presented in [Chen, S. M., & Huang, C. M. (2003). Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms. IEEE Transactions on Fuzzy Systems, 11(4), 495–506].     

基于多表关联的关系数据库多空值估计方法

由于客观世界的复杂性,信息缺失、不确定是普遍存在的。数据库作为表达现实世界的一种工具,使用空值来表达信息缺失的现象。针对关系数据库中的空值问题,提出一种基于多表关联的多空值估计方法。该方法首先以尽可能少地引入误差的原则确定估计每一列空值的顺序;然后对每一列空值先采用本表的信息进行估计,当预测误差大于给定阈值时,根据该表与其他表的关系形式选择不同的模式引入多表信息来提高预测的准确度。实验结果表明该方法估计空值的效果与其他方法相比有较高的准确率。      

空值环境下关系数据库的更新 Ⅰ：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．     

SQL语言中的空值问题

结构化查询语言(SQL)是关系数据库中重要的查询语言，同时由于关系数据库中空值存在的必要性，因此在对不完全信息数据库进行SQL操作时，如何正确地处理空值是一个很重要的研究方向。文中简述了SQL语言标准中对空值的描述，从理论角度分析了引入空值后SQL语言的语义变化，同时从应用角度阐述了引入空值后所引发的一系列问题，最后介绍了下一代数据库语言标准SQL3对NULL处理方法的扩充。由于现实数据库信息的不完全性，文中对于如何正确地认识SOL语言中的空值问题具有很大的借鉴意义。     

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.     

An efficient method for estimating null values in relational databases

Generally, a database system containing null value attributes will not operate properly. This study proposes an efficient and systematic approach for estimating null values in a relational database which utilizes clustering algorithms to cluster data, and a regression coefficient to determine the degree of influence between different attributes. Two databases are used to verify the proposed method: (1) Human resource database; and (2) Waugh's database. Furthermore, the mean of absolute error rate (MAER) and average error are used as evaluation criteria to compare the proposed method with other methods. It demonstrates that the proposed method is superior to existing methods for estimating null values in relational database systems. Jia-Wen Wang was born on September 5, 1978, in Taipei, Taiwan, Republic of China. She received the M.S. degree in information management from the National Yunlin University of Science and Technology, Yunlin, Taiwan, in 2003. Since 2003, she has been a PhD degree student in Information Management Department at the National Yunlin University of Science and Technology. Her current research interests include fuzzy systems, database systems, and artificial intelligence. Ching-Hsue Cheng received the B.S. degree in mathematics from Chinese Military Academy, Taiwan, in 1982, the M.S. degree in applied mathematics from the Chung Yuan Christian University, Taiwan, in 1988, and the Ph.D. degree in system engineering and management from National Defence University, Taiwan, in 1994. Currently, he is a professor of the Department of Information Management, National YunLin University of Technology & Science. His research interests are in decision science, soft computing, software reliability, performance evaluation, and fuzzy time series. He has published more than 120 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with government, and other research-sponsoring agencies.     

An interactive tool for recognizing structured figures with variable shapes

In electric power supply, railway, and other companies with many facilities, facility management is a laborious task. To realize a computerized facility management system, numerous paper-based facility maps should be stored in a database. In this paper, we present a system that constructs a facility management database by interpretation of paper-based facility maps. This system can automatically recognize structured figures with variable shapes on maps, while conventional methods cannot recognize these figures. And this system can easily generate relational data between facilities and character strings on maps. We compare our recognition method of structured figures with variable shapes with a conventional recognition method, and show the effectiveness of our system. Received: 18 November 1996 / Accepted: 16 February 1998     

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。     

一种有效的估计关系数据库中空值的方法

由于客观世界的复杂性,信息缺失、不确定信息是普遍存在的。数据库作为表达现实世界的一种工具,使用空值来表达信息缺失的问题。针对关系数据库中的空值问题,提出一种基于模糊聚类和线性回归的空值估计方法。该方法首先对数据表中的数据进行挖掘,找出与被估计属性相关联的属性集。该过程仅利用数据本身提供的信息,避免了由专家决定条件属性时由于主观性造成的误差。其次根据所得属性集进行模糊聚类得到对原始数据的一个划分,再基于所得分簇和线性回归给出一个估计关系表中空值的方法。最后利用平均绝对错误率来衡量算法估值的准确率。实验结果表明该方法估值的结果与其他方法相比具有较高的准确率。     

Generating weighted fuzzy rules from relational database systems for estimating values using genetic algorithms

In recent years, some methods have been proposed to estimate values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating values. The proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers.     

基于语义视图的SPARQL-SQL查询转换方法

针对油气井工程领域关系数据库,提出一种基于语义视图的SPARQL-SQL查询转换方法.该方法采用特定本体描述数据源关系模式,通过RDF三元组形式,将关系表定义为领域本体之上的语义查询视图,从而建立数据源与本体之间的映射关系,并根据语义映射信息,将提交的SPARQL语句进行解析与查询重写,转换为面向关系数据源的SQL语句.通过实现油气井虚拟数据中心,验证了该方法的可行性与有效性,并获得了良好的应用效果.     

Using Multivalued Logic in Relational Database Containing Null Value

In this paper,several kinds of multivalued logic for relational database and their developing process are presented on the basis of null value‘s semantics.A new 5 valued logic is led into relational database containing null value.The feasibility and necessity of using 5 valued logic are expounded.Comparative calculation and logical calculation under 5 valued logic are defined at the end of the paper.     

基于数据库分组技术的决策树算法的研究

提出了一种利用数据库分组记数技术构造决策树的算法，利用数据库系统的结构化查询语言来实现主要的样本统计计算任务。并将构造决策树的过程与相关属性的选择方法有机地结合在一起。使用这些方法和策略，该算法能较快速地生成决策树，与ID3算法相比，在不影响决策树分类准确度的前提下，具有较高的执行效率。     

基于多重表的关系模式到DTD的转换

提出一种基于多重表的关系模式到DTD的转换方法．该方法不仅使转换后的DTD使用了元素内容模型的正则表达式，而且能够保持关系数据库中各表间的关系信息，使生成的DTD体现了层次组织结构的特点。     

Functional dependencies with null values, fuzzy values, and crispvalues

Based on the concepts of the semantic proximity, we present a definition of the fuzzy functional dependency, We show that the inference rules for fuzzy functional dependencies, which are the same as Armstrong's axioms for the crisp case, are correct and complete. We also show that dependent constraints with dull values constitute a lattice. Functional dependencies in classical relational databases and null functional dependencies can be viewed as a special case of fuzzy functional dependencies. By applying the unified functional dependencies to the relational database design, we can represent the data with fuzzy values, null values and crisp values under relational database management systems, By using fuzzy functional dependencies, we can compress the range of a fuzzy value and make this fuzzy value “clearer”     

语义Web中RDF元数据的存储与管理

语义Web的实现首要解决的问题就是对资源的描述,RDF是描述信息资源的基础,管理和存储RDF数据成为必须要解决的问题。如果把RDF数据存储在关系数据库中,就可以有效地利用现有的数据库资源来管理RDF数据。本文用垂直Scheme的格式来构造RDF数据存储表,通过模式映射把RDF数据映射成RDF数据存储表中的记录;此外又给出了用RDF视图查询RDF数据的方法为实现语义查询打下了基础。