Beyond the relational database model

The relational model is a set-theoretic model for describing data constructs common in the business environment. Relational databases also minimize data duplication, which ensures data integrity and reduces storage requirements. Further, the relational model provides a way to represent variable-length constructs with fixed-length components. In addition, normalization theory is the basis of hundreds of papers and successful tenure applications. This ensured the academic community would carry the model forward. Finally, by following open standards, including the structured query language, vendors created a buzz with dozens of relational DBMS products such as System R, DB2, Oracle, SQL Server, Ingres, dBase, R:Base, Pearl, Paradox, and Access. An XDS have many advantages over a relational database, including seamless integration with user views as well as all the benefits of XML standards such as XML schema validation and the extensible stylesheet language for document materialization.     

抽取XML文档到关系数据库

XML在关系数据库中的存储问题是XML研究领域中的一个重要问题。在总结多种映射方法的基础上,提出了一种方法将多个相似的XML文档进行解析,根据映射关系,生成各自的关系模式,并分析归纳出一个集成的关系模式,然后创建一个关系数据库,并在映射关系的基础上提取并存储XML文档数据到关系数据库。此方法以较为简洁的结构保存了XML文档的数据信息,其最大的特点就是不用考虑文档的模式信息（DTD,XML Schema）。并通过一个具体的实验结果来说明这种方法的有效性。     

利用关系数据库实现无模式XML数据管理平台

基于目前无模式XML数据被广泛应用的现状,研究了如何利用关系数据库对无模式XML数据进行统一管理,设计并验证了一种基于关系数据库的统一无模式XML数据管理平台。分析了什么是无模式XML数据,介绍了管理平台的总体结构,详细叙述了管理平台的设计思想和实现技术,结合实际的应用项目验证了无模式XML数据管理平台。     

Uncertainty in a nested relational database model

Some database models have already been developed to deal with complex values but they have constrains that data stored is precise and queries are crisp. However, as many researchers have pointed out, there is a need to present, manipulate, and query complex and uncertain data of various non-traditional database applications such as oceanography, multimedia, meteorology, office automation systems, engineering designs, expert database systems and geographic information systems. In this paper, we present a logical database model, which is an extension of a nested relational data model (also known as an NF² data model), for representing and manipulating complex and uncertain data in databases. We also introduce a possible physical representation of such complex and uncertain values in databases and describe the query processing of the model that we discuss here.     

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     

Mining temporal interval relational rules from temporal data

Temporal data mining is still one of important research topic since there are application areas that need knowledge from temporal data such as sequential patterns, similar time sequences, cyclic and temporal association rules, and so on. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from temporal interval data such as patient histories, purchaser histories, and web logs etc. We propose a new temporal data mining technique that can extract temporal interval relation rules from temporal interval data by using Allen’s theory: a preprocessing algorithm designed for the generalization of temporal interval data and a temporal relation algorithm for mining temporal relation rules from the generalized temporal interval data. This technique can provide more useful knowledge in comparison with conventional data mining techniques.     

Protein simulation data in the relational model

High performance computing is leading to unprecedented volumes of data. Relational databases offer a robust and scalable model for storing and analyzing scientific data. However, these features do not come without a cost??significant design effort is required to build a functional and efficient repository. Modeling protein simulation data in a relational database presents several challenges: The data captured from individual simulations are large, multidimensional, and must integrate with both simulation software and external data sites. Here, we present the dimensional design and relational implementation of a comprehensive data warehouse for storing and analyzing molecular dynamics simulations using SQL Server.     

Using UML to model relational database operations

The Unified Modeling Language (UML) is being used as the de-facto standard in the software industry. With the adoption of UML 2.0, the new enhancements allow this version to describe many of the elements found in today's software technology as well as Model Driven Architecture and Service-Oriented Architecture. Although the Object Management Group (OMG) has released several UML Profiles to tailor the language to specific areas, relational database modeling is not fully addressed in these profiles. Many existing software applications involve complex application layer implemented in object-oriented programming languages and at the same time use relational database systems as the back-end data store. Modeling the whole system in a consistent manner will help developers and end users better understand the application. In this work we show how to model relational database operations using UML. Atomic database operations are modeled based on our framework and are used as building blocks to model more complex database operations.     

关系数据库中多维数据分析及展现的研究

在对关系数据库业务模型分析的基础上通过建立多维模型定义使数据具有多维的概念,并在此基础上提供了一种B/S模式的多维数据查询和展现的方法,该方法充分利用了多种数据存储结构来优化数据的查询和显示.通过建立维度间的层次关系可实现多维数据的钻取、旋转、切片等操作,结合JFreeChart的图形功能提供了一种多维图形展现和分析的方法.     

基于XML技术的异构关系数据库集成模型

为了有效解决网络环境中异构数据库数据集成问题,分析了异构数据库集成的现状,提出了一种基于XML技术的异构关系数据库数据集成模型.该模型由异构数据库层、集成中间件层,应用层3部分组成;阐述了该模型各个部分的功能,讨论了集成中间件层的实现;详细地介绍了数据库和XML之间相互映射规则,并给出了它们之间的具体转换过程.在集成中间件的基础上,设计了某信息管理服务系统软件,表明了该中间件的可行性.     

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.     

基于关系数据库的半结构化XML数据组织技术

XM L数据的组织和管理存在多种方法。描述XM L数据、模式以及半结构化XM L数据特点,介绍XM L数据库与XM L数据存储方法,讨论XM L数据在关系数据库中的组织策略;基于编码机制,将半结构化XM L 编码分成次序编码、路径编码、混合编码和支持更新的扩展编码,分析总结半结构化XM L数据编码技术,展望XM L数据组织技术的发展趋势,为进一步进行XM L数据组织技术研究提供基础。     

Another view of functional and multivalued dependencies in the relational database model

In this paper, pseudo-functional and pseudo-multivalued dependencies are introduced. They are shown to be isomorphic with functional and multivalued dependencies, i.e., they behave in the same way with respect to implication. This formalism leads in a very natural way to a rather efficient algorithm for the inference of functional and multivalued dependencies. Some applications to acyclic join dependencies are discussed.     

Efficient recursive XML query processing using relational database systems

Recursive queries are quite important in the context of XML databases. In addition, several recent papers have investigated a relational approach to store XML data and there is growing evidence that schema-conscious approaches are a better option than schema-oblivious techniques as far as query performance is concerned. However, the issue of recursive XML queries for such approaches has not been dealt with satisfactorily. In this paper we argue that it is possible to design a schema-oblivious approach that outperforms schema-conscious approaches for certain types of recursive queries. To that end, we propose a novel schema-oblivious approach, called Sucxent++ (Schema Unconcious XML Enabled System), that outperforms existing schema-oblivious approaches such as XParent by up to 15 times and schema-conscious approaches (Shared-Inlining) by up to eight times for recursive query execution. Our approach has up to two times smaller storage requirements compared to existing schema-oblivious approaches and 10% less than schema-conscious techniques. In addition Sucxent++ performs marginally better than Shared-Inlining and is 5.7–47 times faster than XParent as far as insertion time is concerned.     

Efficient tracking of moving objects using a relational database

Tracking uncooperative moving objects by means of radar is a complex task due to clutter and association problems in multi-target scenarios. An approach to solve this problem is probabilistic multiple hypothesis tracking (PMHT). This method combines classical track filtering with a likelihood ratio test for the estimation of the plot-to-track association. The basics of PMHT and similar algorithms have gained much attention recently. However, the efficient implementation of real world applications of this technique still represents a challenging task. Since a common requirement in this context is the reliable storage of track data in a database, an implementation of the tracker's calculation inside a database management system (DBMS) using SQL views is desirable. A naive implementation of PMHT using a commercial DBMS, however, usually leads to performance problems because of the high frequency of measurement updates. In this paper, we propose possible optimizations for solving these performance problems. Their usage leads to a dramatic run-time improvement in our sample case and makes the implementation of PMHT in a database context feasible.     

New algorithms for parallelizing relational database joins in thepresence of data skew

Parallel processing is an attractive option for relational database systems. As in any parallel environment however, load balancing is a critical issue which affects overall performance. Load balancing for one common database operation in particular, the join of two relations, can be severely hampered for conventional parallel algorithms, due to a natural phenomenon known as data skew. In a pair of recent papers (J. Wolf et al., 1993; 1993), we described two new join algorithms designed to address the data skew problem. We propose significant improvements to both algorithms, increasing their effectiveness while simultaneously decreasing their execution times. The paper then focuses on the comparative performance of the improved algorithms and their more conventional counterparts. The new algorithms outperform their more conventional counterparts in the presence of just about any skew at all, dramatically so in cases of high skew     

基于本体理论的关系数据库存储模式

通过分析借鉴现有本体在关系数据库中的存储模式的优势与不足,提出面向查询的基于本体理论的关系数据库新型存储模式.分析关系数据库概念模型与OWL本体表述之间的关系,实现OWL本体描述关系数据库概念模型,将本体映射到POJO对象模型,利用ORM技术将对象模型映射到关系模型中,形成新型的关系数据库存储模式.该存储模式支持数据库概念模型的灵活演变与更新,实验验证了其有效性.     

用于数据交换的XML文档和关系数据库转换

XML以其结构性、标签性、可扩展性和可移植性成为网络数据交换方面的标准技术,广泛地应用于计算机软件,尤其是电子商务应用领域。但目前数据存储和管理的主流手段仍是成熟而稳健的数据库。这就决定了用于数据交换的XML文档与数据库之间必须频繁的进行转换。在介绍了XML带来的“用数据库存储、XML文档传输”的全新工作方式之后,提出了一种利用良好数据结构和递归算法提取XML文档节点,进而把XML文档转换为关系数据库的方法。最后介绍了一个转换平台,验证了算法的可行性。