关系数据库数字水印技术

随着知识产权保护和信息安全技术的发展,关系数据库数据存在所有权问题,可以采用数字水印技术对其进行数字所有权管理。该文分析了关系数据库中的数据和图像、声频、视频等多媒体数据的区别及关系数据库水印的特点,探讨了几种关系数据库数字水印技术,还分析了对关系数据库数据水印技术的攻击方式和理想的关系数据库水印技术。水印关系数据库对数据库的安全有着广阔前景和实际应用价值。     

一种基于关系数据库的脆弱性水印算法

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,在对分组内的所有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,从而可以将对数据库的篡改定位在分组范围内.理论分析和实验结果表明了该方法的有效性和可行性.     

A Novel Watermarking Scheme for Secure Relational Databases

ABSTRACT

Digital watermarking for relational databases emerged as a candidate solution to provide copyright protection and tamper detection and to maintain the integrity of data. Sectors such as defense, finance, and human resources require reliable schemes, especially for data alteration and integrity checking. Many watermarking techniques have been proposed in the literature to address the above-mentioned problems subject to their limitations on numeric attributes. However, these techniques do not yield satisfactory results in the case of small numeric values. To overcome these limitations, we have proposed a watermarking scheme in which the third most significant bit of numeric and nonnumeric attributes is replaced with the watermark bit without loss of originality. The proposed scheme must be suitable for the above-mentioned sectors, including commercial banks where fraud and forgery cases are common.     

(对象)关系数据库中XML文档的存储技术

XML逐渐成为Web上数据表示和数据交换的标准，随着Web上大量的数据用XML文档表示出来，有必要对这些XML文档进行存储和查询。目前大多数商业数据库产品都支持对XML文档的存储、查询、索引等操作，本文讨论了在ORDB中存储XML文档时所涉及的存储技术，比较了三大商业数据库产品（IBM DB2，Oracle9i,Microsoft SQL Server 2000)在存储XML文档时所采用的方法。     

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.     

一种基于RDBMS的XML数据的存储方法

XML作为一种数据交换的标准在互联网上推出,使得XML数据和数据库的相互交换成为必要:一是因为WEB中大量的多样化数据需要进行有效的存储和管理;二是因为在现有的数据库中存储有大量的数据并且需要将这些数据转换为XML发布到WEB中。论文提出了一个基于关系数据库的数据转换框架,基于数据的完整性讨论XML数据存储策略。建立一个XML通用数据模型,把文档树分解成多个节点,根据一定的映射规则存储到关系表中,从而不用考虑文档的模式信息(DTD、XMLSchema)。最后通过一个具体的文档实例来说明这种策略的有效性。     

Multimedia Digital Rights Protection Using Watermarking Techniques

ABSTRACT

With the advancement of Internet technologies and its wide usage, it has become easy to illegally copy, distribute, and manipulate digital products. Digital watermarking is a proven method to protect authenticity by identifying the owner of the digital content. Significant work has been reported in the last five years on legal and technical measures for protecting digital rights. This paper explains the concepts of ownership rights and related intellectual property rights and their technical and legal protection measures. It also introduces digital water marking, its classification, features, and applications.     

基于关系数据库的关键词查询

介绍了基于关系数据库的关键词查询问题的研究背景;阐述了解决该问题的两大类方法,即基于数据图的方法和基于模式图的方法,并详细介绍了各种方法的原理以及各自的优缺点;最后展望了未来的研究方向.     

Test-Driven Development of Relational Databases

In test-first development, developers formulate and implement a detailed design iteratively, one test at a time. Test-driven development (also called test-driven design) combines TFD with refactoring, wherein developers make small changes (refactorings) to improve code design without changing the code's semantics. When developers decide to use TDD to implement a new feature, they must first ask whether the current design is the easiest possible design to enable the feature's addition. Implementing test-driven database design involves database refactoring, regression testing, and continuous integration. TDDD is an integrated part of the overall development process, not a standalone activity that data professionals perform in parallel with application TDD. Although from a technical view point, TDDD is relatively straightforward, we must overcome several challenges to its whole sale adoption throughout the IT community     

关系数据库的关键词检索

SQL查询语言被用于检索关系数据库中的数据,但对于没有经验的用户来说,学习复杂的SQL语法是一件困难的事情。实现基于关键词的关系数据库信息检索,将使用户不需要任何SQL语言和底层数据库模式的知识,用搜索引擎的方式来获取数据库中的相关数据。本文总结了基于关键字的数据库检索工作的关键技术和研究进展,展望了今后的研究方向。     

Object-Oriented Databases with Relational DBMSs

The expediency of the use of object-oriented models of data as datalogical models in designing databases is shown, and methods of integration of relational and object-oriented models are considered. A relational scheme for storage of attributes and relations of object-oriented models is proposed.     

Graph Transformation in Relational Databases

We present a novel approach to implement a graph transformation engine based on standard relational database management systems (RDBMSs). The essence of the approach is to create database views for each rules and to handle pattern matching by inner join operations while negative application conditions by left outer join operations. Furthermore, the model manipulation prescribed by the application of a graph transformation rule is also implemented using elementary data manipulation statements (such as insert, delete, update).     

Null Inclusion Dependencies in Relational Databases

Functional dependencies (FDs) and inclusion dependencies (INDs) are the most fundamental integrity constraints that arise in practice in relational databases. We introduce null inclusion dependencies (NINDs) to cater for the situation when a database is incomplete and contains null values. We show that the implication problem for NINDs is the same as that for INDs. We then present a sound and complete axiom system for null functional dependencies (NFDs) and NINDs, and prove that the implication problem for NFDs and NINDs is decidable and EXPTIME-complete. By contrast, when no nulls are allowed, this implication problem is undecidable. This undecidability result has motivated several researchers to restrict their attention to FDs and noncircular INDs in which case the implication problem was shown to be EXPTIME- complete. Our results imply that when considering nulls in relational database design we need not assume that NINDs are noncircular.     

Mining Short-Rule Covers in Relational Databases

An implication rule Q → R is a statement of the form "for all objects in the database, if an object has the attribute–value pairs Q then it has also the attribute–value pairs R ." This simple type of rule is theoretically interesting, because it supports reasoning, similar to functional dependencies in database theory, and it may be of practical significance because the size of the set of implication rules that hold in a relation can remain substantially high even when mining real data and considering only most general covers; i.e., covers containing rules with unredundant right and left sizes. Motivated by these observations, we focus on the extraction of short-rule covers, which cannot be efficiently mined by standard rule miners. We present an algorithm driven by "negative examples" (i.e., satisfy Q but not R ) to prune the rule-candidate lattice associated with each "positive example" (i.e., satisfies both Q and R ). The algorithm scales up quite well with respect to the number of objects and it is particularly suitable for databases with attributes described by large domains. Furthermore, a perfect hash function ensures extraction of short-rule covers even from databases containing a large number of attributes.     

Multidimensional Index Structures in Relational Databases

Efficient query processing is one of the basic needs for data mining algorithms. Clustering algorithms, association rule mining algorithms and OLAP tools all rely on efficient query processors being able to deal with high-dimensional data. Inside such a query processor, multidimensional index structures are used as a basic technique. As the implementation of such an index structure is a difficult and time-consuming task, we propose a new approach to implement an index structure on top of a commercial relational database system. In particular, we map the index structure to a relational database design and simulate the behavior of the index structure using triggers and stored procedures. This can be easily done for a very large class of multidimensional index structures. To demonstrate the feasibility and efficiency, we implemented an X-tree on top of Oracle8. We ran several experiments on large databases and recorded a performance improvement up to a factor of 11.5 compared to a sequential scan of the database.     

粗关系数据库查询模型

针对粗关系数据库中数据的特性,提出一种不确定性数据的存储方法。基于汉明距离的一种变式,计算元组间距离,构成距离矩阵,根据距离矩阵将相同或相近的元组归类,从而有效地对表中的元组进行索引。借助粗集中的上、下近似,通过计算用户查询的数据与粗关系数据库中数据的相似度,查询出用户所需的数据。结合以上方法构建粗关系数据库查询模型,设计相应的查询算法并应用于实例中。     

Table Oriented Metrics for Relational Databases

Developing and selecting high quality software applications are fundamental. It is important that the software applications can be evaluated for every relevant quality characteristic using validated metrics. Software engineers have been putting forward hundreds of quality metrics for software programs, disregarding databases. However, software data aspects are important because the size of data and their system nature contribute to many aspects of a systems quality. In this paper, we proposed some internal metrics to measure relational databases which influence its complexity. Considering the main characteristics of a relational table, we can propose the number of attributes (NA) of a table, the depth of the referential tree (DRT) of a table, and the referential degree (RD) of a table. These measures are characterized using measurement theory, particularly the formal framework proposed by Zuse. As many important issues faced by the software engineering community can only be addressed by experimentation, an experiment has been carried out in order to validate these metrics.     

基于关系数据库的XBRL存储系统

可扩展商业报告语言(XBRL)是一种专门用于金融财政系统之间进行数据交换的XML扩展语言。该文介绍了一种基于关系数据库的XBRL存储系统：X-SIR系统。该系统可以根据XBRL数据模型自动生成关系数据模式,把XBRL实例文档中的数据自动转换成数据库表中的数据记录。     

XML文档在关系数据库中的规范化存储

提出了一种存储方法，首先把XML文档映射为泛关系模式，再利用算法DeriveFDs推导出XML键所蕴含的泛关系模式上函数依赖集的规范覆盖，根据此规范覆盖，最后将泛关系模式保持函数依赖地分解为3NF模式集。得到了保持XML键约束的规范化存储模式，实现了XML文档在关系数据库中的规范化存储。实验研究表明文中提出的方法是有效的。     

面向关系数据库的智能索引调优方法

数据库索引是关系数据库系统实现快速查询的有效方式之一.智能索引调优技术可以有效地对数据库实例进行索引调节,从而保持数据库高效的查询性能.现有的方法大多利用了数据库实例的查询日志,它们先从查询日志中得到候选索引,再利用人工设计的模型选择索引,从而调节索引.然而,从查询日志中产生出的候选索引可能并未实际存在于数据库实例中,因此导致这些方法不能有效地估计这类索引对于查询的优化效果.首先,设计并实现了一种面向关系数据库的智能索引调优系统;其次,提出了一种利用机器学习方法来构造索引的量化模型,根据该模型,可以准确地对索引的查询优化效果进行估计;接着设计了一种高效的最优索引选择算法,实现快速地从候选索引空间中选择满足给定大小约束的最优的索引组合;最后,通过实验测试不同场景下智能索引调优系统的调优性能.实验结果表明,所提出的技术可以在不同的场景下有效地对索引进行优化,从而实现数据库系统查询性能的提升.