一种基于XML的半结构化数据存储方法

提出了一种基于XML存储半结构化数据的方法，设计并实现了相应存储与解析算法。鉴于OEM模型是一种图状模型，而通常的XML据模型是树状模型，为此需解决图状模型数据映射为树状模型数据这一关键问题，利用XML元素和属性的语义信息可从语义级别解决该问题。，     

一种支持XML的文件系统构建模型

XML作为数据存储和传输的标准,已经广泛应用于网络和数据库等数据处理系统中.针对大量出现的XML数据,传统的基于字节流的文件系统因其不具备存储和管理语义结构的能力,只能将XML数据视为普通的文本文档进行存储和管理,因而不能充分利用XML的半结构化等优点进行高效的细粒度的信息管理.本文在研究基于XML的数据库系统和语义文件系统的基础上,提出一种新型的基于XML的文件系统模型,并在传统文件系统上利用目录和文件分别存储XML文件的元素和属性实现一种语义结构的存储,使文件系统具备对信息的细粒度控制.     

基于XML的非结构化文本数据转换研究与实现

采用XML作为存储数据的中间过程,通过两次调用Java程序,使非结构化的数据结构化因为XML作为一种半结构化的语言,适合数据存储与数据转换 而Java程序可以让基于XML的非结构化数据转换成结构化的数据,使其完全的结构化.使用该Java程序,可以完成类似的非结构化数据的转换工作.     

基于本体的XML关联规则挖掘方法

针对传统的数据挖掘技术不能直接应用到半结构化的XML数据挖掘问题,给出了基于本体的XML关联规则挖掘方法。该方法引入领域本体和哈希技术来改进产生频繁项目集的操作和生成关联规则的操作,并且使用哈希表存储相关的领域本体,从而将数据库操作转化为对XML内存树的操作,通过理论分析和实验验证了方法的挖掘效果,论述了该方法的优点。     

扩展哈弗曼前缀编码实现XML数据与关系数据转换

为从企业生产线上XML半结构化数据中抽取富有意义数据,分析了XML半结构化数据和关系数据库中结构化数据特点,以及XML半结构化数据在关系数据库中的存储方法。针对实际应用,提出采用扩展哈弗曼前缀编码方法,对XML文档树进行唯一编码,实现XML文档与关系数据库映射,同时给出最长前缀匹配策略,支持数据查询,以提高查询效率。     

一个XML的数据模型及其存储策略

XML是用于数据表示、交换的Internet标准。通过和DTD的连接可以用像XML－QL这样的语言来执行丰富的查询操作。近年来,很多人致力于半结构化数据模型和其查询语言的研究^[1,2,5],其重点逐渐转移到XML数据集的查询上来,其中两个重要问题是使XML查询语言正规化和如何将XML数据转换为底层存储格式以获得理想的效率^[4]。表述了一个XML的正规数据模型及其代数方法,并介绍基于RDBMS实现该模型的方法。     

基于关系的XML数据存储技术

XML的数据存储是处理XML数据的首要问题。XML的主要存储方式为文件系统,关系数据库,面向对象数据库和半结构化数据仓库等,这里重点讨论了基于关系的XML存储技术。     

XML数据存储方法研究及应用

随着互联网上半结构化数据的迅速增长,可扩展标记语言(XML)已逐渐成为Web上数据表示和数据交换的标准,对XML数据进行有效的存储是数据管理的一个核心问题。文中论述了XML数据在文件系统、关系数据库、面向对象数据库和Native-XML数据库中的存储方法,分析比较了各种存储方法的优缺点,并给出了一个在教育资源库中的应用实例。     

基于模式的XML文档关系数据库存储

基于XML的模式定义给出了一种使用关系数据库存储XML文档的方法．谊方法在存储文档的同时，使用元表以保存模式信息，并实现了模式所定义的完整性约束．对查询映射和由于XML的半结构化特性所导致的一些目前难以解决的问题，文章也进行了说明．     

XML数据存储方法研究及应用

随着互联网上半结构化数据的迅速增长，可扩展标记语言（XML）已逐渐成为web上数据表示和数据交换的标准，对XML数据进行有效的存储是数据管理的一个核心问题。文中论述了XML数据在文件系统、关系数据库、面向对象数据库和Native-XML数据库中的存储方法，分析比较了各种存储方法的优缺点，并给出了一个在教育资源库中的应用实例。     

Child body shape measurement using depth cameras and a statistical body shape model

We present a new method for rapidly measuring child body shapes from noisy, incomplete data captured from low-cost depth cameras. This method fits the data using a statistical body shape model (SBSM) to find a complete avatar in the realistic body shape space. The method also predicts a set of standard anthropometric data for a specific subject without measuring dimensions directly from the fitted model. Since the SBSM was developed using principal component (PC) analysis, we formulate an optimisation problem to fit the model in which the degrees of freedom are defined in PC-score space. The mean unsigned distance between the fitted-model based on depth-camera data and the high-resolution laser scan data was 9.4 mm with a standard deviation (SD) of 5.1 mm. For the torso, the mean distance was 2.9 mm (SD 1.4 mm). The correlations between standard anthropometric dimensions predicted by the SBSM and manually measured dimensions exceeded 0.9.     

Parametric body shape model of standing children aged 3–11 years

A statistical body shape model (SBSM) for children was developed for generating a child body shape with desired anthropometric parameters. A standardised template mesh was fit to whole-body laser scan data from 137 children aged 3–11 years. The mesh coordinates along with a set of surface landmarks and 27 manually measured anthropometric variables were analysed using principal component (PC) analysis. PC scores were associated with anthropometric predictors such as stature, body mass index (BMI) and ratio of erect sitting height to stature (SHS) using a regression model. When the original scan data were compared with the predictions of the SBSM using each subject's stature, BMI and SHS, the mean absolute error was 10.4 ± 5.8 mm, and 95th percentile error was 24.0 ± 18.5 mm. The model, publicly available online, will have utility for a wide range of applications.

Practitioner Summary: A statistical body shape model for children helps to account for inter-individual variability in body shapes as well as anthropometric dimensions. This parametric modelling approach is useful for reliable prediction of the body shape of a specific child with a few given predictors such as stature, body mass index and age.     

Novel Similarity Measures for Differential Invariant Descriptors for Generic Object Retrieval

Local feature matching is an essential component of many image and object retrieval algorithms. Euclidean and Mahalanobis distances are mostly used in order to quantify the similarity of two stipulated feature vectors. The Euclidean distance is inappropriate in the typical case where the components of the feature vector are incommensurable entities, and indeed yields unsatisfactory results in practice. The Mahalanobis distance performs better, but is less generic in the sense that it requires specific training data. In this paper we consider two alternative ways to construct generic distance measures for image and object retrieval, which do not suffer from any of these shortcomings. The first approach aims at obtaining a (image independent) covariance matrix for a Mahalonobis-like distance function without explicit training, and is applicable to feature vectors consisting of partial image derivatives. In the second approach a stability based similarity measure (SBSM) is introduced for feature vectors that are composed of arbitrary algebraic combinations of image derivatives, and likewise requires no explicit training. The strength and novelty of SBSM lies in the fact that the associated covariance matrix exploits local image structure. A performance analysis shows that feature matching based on SBSM outperforms algorithms based on Euclidean and Mahalanobis distances.     

A Z Approach in Validating ORA-SS Data Models

The rapid growth of the World Wide Web has resulted in more data being accessed over the Internet. In turn there is an increase in the use of semistructured data, which plays a crucial role in many web applications particularly with the introduction of XML and its related technologies. This increase in use makes the design of good semistructured data structures essential. The Object Relationship Attribute model for Semistructured data (ORA-SS) is a graphical notation for designing and representing semistructured data. In this paper, we demonstrate an approach to formally validate the ORA-SS data models in order to enhance the correctness of semistructured data design. A mathematical semantics for the ORA-SS notation is defined using the Z formal language, and further validation processes are carried out to check the correctness of the semistructured data models at both the schema and instance levels.     

半结构化数据查询的处理和优化

半结构化数据的特点是数据的结构不规则或不完整,其模型都基于带根有向图,因此,查询处理过程本质上是对图的搜索过程.另外,通配路径使查询处理更加复杂化.文章详细介绍了异构数据源集成系统Versatile中采取的半结构数据OIM(model for object integration)对象的查询和优化策略,包括查询计划的生成、路径扩展和路径索引、层次索引和基于数据源知识这3种查询优化方法.文章介绍的方法同样适用于其他的半结构化数据模型.     

构造Web文档中半结构化信息的技术

为了对Ｗｅｂ上不规则的、动态的信息按照数据库的方式集成和查询,采用对象交换模型（ＯＥＭ）建立了Ｗｅｂ上信息模型。为了将页面中各个部分表示为应用的ＯＥＭ对象,设计了半结构化信息的抽取算法,并给出测试结果。该方法可以抽取结构化和半结构化的信息,比现有的抽取方法通用性更强。     

Theorem prover approach to semistructured data design

The wide adoption of semistructured data has created a growing need for effective ways to ensure the correctness of its organization. One effective way to achieve this goal is through formal specification and automated verification. This paper presents a theorem proving approach towards verifying that a particular design or organization of semistructured data is correct. We formally specify the semantics of the Object Relationship Attribute data model for Semistructured Data (ORA-SS) modeling notation and its correctness criteria for semistructured data normalization using the Prototype Verification System (PVS). The result is that effective verification on semistructured data models and their normalization can be carried out using the PVS theorem prover.     

基于模型检测的半结构化数据查询

在Web迅猛发展和半结构化数据急剧膨胀的今天,Web上的半结构化数据查询日益成为信息技术领域一个重要的研究发展方向。现有的半结构化查询语言在查询效率上存在着不足。文章提出了一个将模型检测技术应用到半结构化数据查询的新方法,它通过把数据库看作Kripke状态变迁系统,把查询解释为时序逻辑CTL公式,将查询过程转化为模型对公式的满足问题,通过模型检测过程高效地完成数据查询,从而使查询可以在多项式时间内完成,很好地改善了查询效率。最后,给出了该方法在现有半结构化查询语言Lorel上的应用。     

半结构化数据模型及查询语言

在传统数据库中要求查询处理时数据的结构模式已知且固定。这在ＷＷＷ和异构信息源集成等半结构化数据情形下很难满足。     

基于Web的快速信息抽取

介绍了一种基于Web的信息抽取的快速实现方法,该方法将信息抽取划分为两个阶段,在每个阶段采用不同的数据模型,对于半结构和无结构的信息抽取都有很好的效果。