为实现XML数据库的性能评测,提出基于TPC-C的XML数据库测试方案。针对XML数据库特性,对其数据结构、查询事务语句进行定制,将原有9张表映射成5个XML Schema文件,按照SQL/XML标准重写负载事务。应用该方案对SQL Server 2005数据库进行测...为实现XML数据库的性能评测,提出基于TPC-C的XML数据库测试方案。针对XML数据库特性,对其数据结构、查询事务语句进行定制,将原有9张表映射成5个XML Schema文件,按照SQL/XML标准重写负载事务。应用该方案对SQL Server 2005数据库进行测试,结果表明显示的各项事务特征均与TPC-C基准相同。展开更多
Tree Match算法是一种有效的Twig查询匹配算法,但其存在反复分析Twig模式的缺点。针对该问题,引入编译中的部分求值技术,提出一种Twig查询优化方案。通过部分求值提前完成对Twig模式的分析,生成查询专用的指令序列代替原查询程序,并给...Tree Match算法是一种有效的Twig查询匹配算法,但其存在反复分析Twig模式的缺点。针对该问题,引入编译中的部分求值技术,提出一种Twig查询优化方案。通过部分求值提前完成对Twig模式的分析,生成查询专用的指令序列代替原查询程序,并给出查询机执行引擎,从而消除重复计算,优化XML树模式查询过程。实验结果表明,在不同Twig模式下,该优化方案能够有效提高XML查询的执行效率。展开更多
A new web product data management architecture is presented. The three-tier web architecture and Simple Object Access Protocol (SOAP) are combined to build the web-based product data management (PDM) system which incl...A new web product data management architecture is presented. The three-tier web architecture and Simple Object Access Protocol (SOAP) are combined to build the web-based product data management (PDM) system which includes three tiers: the user services tier, the business services tier, and the data services tier. The client service component uses the server-side technology, and Extensible Markup Language (XML) web service which uses SOAP as the communication protocol is chosen as the business service component. To illustrate how to build a web-based PDM system using the proposed architecture, a case PDM system which included three logical tires was built. To use the security and central management features of the database, a stored procedure was recommended in the data services tier. The business object was implemented as an XML web service so that client could use standard internet protocols to communicate with the business object from any platform. In order to satisfy users using all sorts of browser, the server-side technology and Microsoft ASP.NET was used to create the dynamic user interface.展开更多
There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ＇big data＇. The structural shift of the storage m...There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ＇big data＇. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mecha- nism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the ex- isling approaches using Brewer＇s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.展开更多
文摘为实现XML数据库的性能评测,提出基于TPC-C的XML数据库测试方案。针对XML数据库特性,对其数据结构、查询事务语句进行定制,将原有9张表映射成5个XML Schema文件,按照SQL/XML标准重写负载事务。应用该方案对SQL Server 2005数据库进行测试,结果表明显示的各项事务特征均与TPC-C基准相同。
基金the National Key Project Foundation of China (No. 2001BA201A0605) and partially supported by the State Key Lab for Mechanical Transmission..
文摘A new web product data management architecture is presented. The three-tier web architecture and Simple Object Access Protocol (SOAP) are combined to build the web-based product data management (PDM) system which includes three tiers: the user services tier, the business services tier, and the data services tier. The client service component uses the server-side technology, and Extensible Markup Language (XML) web service which uses SOAP as the communication protocol is chosen as the business service component. To illustrate how to build a web-based PDM system using the proposed architecture, a case PDM system which included three logical tires was built. To use the security and central management features of the database, a stored procedure was recommended in the data services tier. The business object was implemented as an XML web service so that client could use standard internet protocols to communicate with the business object from any platform. In order to satisfy users using all sorts of browser, the server-side technology and Microsoft ASP.NET was used to create the dynamic user interface.
文摘There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ＇big data＇. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mecha- nism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the ex- isling approaches using Brewer＇s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.
