半结构化数据的最小化模式发现

半结构化数据的模式抽取对于半结构化数据查询、优化及异构数据的集成具有重要的意义.结合标签路径及标签路径的目标集概念,提出了基于OEM(Object Exchange Model)模型的半结构化数据最小化模式抽取新方法,并给出了与标签路径目标集、支持度计算相关的两个定理.算法的基本思路:依据文中的两个定理,采用宽度优先自顶向下的遍历策略依次求出各标签路径的最后一个标签的目标集及支持度,标签支持度大的目标集优先映射为对应的模式节点.对同一半结构数据实例,算法抽取的模式与其他算法得到的模式相比规模小、算法执行时间短.算法适用于层次型及包含环路的OEM半结构化数据模式抽取.     

自动粒度选择的半结构化页面信息抽取

半结构化页面的数据记录间存在结构相似性,在先序遍历DOM树生成的标记序列中表现为重复出现的模式,可利用后缀树进行挖掘。由于标记序列可以在块粒度和文本粒度两个级别上展现,而不同粒度下产生的最佳抽取模式在抽取效果方面又表现出不确定性,因此提出一种自动粒度选择的半结构化页面信息抽取方法。算法从后缀树获取的重复模式中选取最大重复和串联重复构成候选模式集,通过特征参数确定两个粒度各自的最佳模式集,最后引入抽取结果规则度参数并进行综合评价,以确定抽取模式完成半结构化页面数据记录的自动抽取。     

基于约束的半结构化信息的抽取方法

为了对WEB上不规则的动态信息按照数据库的方式集成和查询,本文采用对象交换模型(OEM)建立WEB上信息模型。为了将页面中各个部分表示为对应的OEM对象,本文(1)设计了半结构化信息的抽取算法;(2)定义了满足约束条件的数据抽取格式,并且设计了输出正确抽取格式的候选者算法;(3)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。     

半结构化、层次数据的模式发现

Web数据资源及数据集成引发了半结构化数据问题,半结构化数据指其结构隐含或不规整的自描述数据。由于缺乏独立于数据的模式,有效地查询划浏览该类数据比较困难,半结构化数据的模式发现成为解决该问题的基础步骤。本文提出的算法能够快速有效地发现半结构化层次数据中的规整结构。它采用自顶向下的生成,结合有效的剪枝策略,从OEM模型表达的半结构化层次数据中构建模式树。     

从WEB文档中构造半结构化信息的抽取器

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(object exchange model,简称OEM)建立了WEB信息模型.为了将页面中各个部分表示为对应的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.     

RE-OEM：一种半结构化生物数据的信息抽取模型*

在分析半结构化生物数据特点的基础上,提出了一种新的半结构化数据抽取模型REOEM。它将OEM数据模型和正则表达式有机地结合起来,不但能够灵活方便地表示各种数据结构,而且能够非常方便地进行模式匹配和数据的定位,为半结构化生物数据的抽取打下坚实基础。     

模式发现在Web抽取中的应用及设计

WWW的迅速发展，使其日益成为人们查找有用数据的重要来源。但因每个Web站点的主题各异、形式多样、结构不同，人们往往要花大量精力在人工定位和抽取有用数据上。提出了一个基于模式发现的数据抽取框架，分析了将PAT树用于模式发现的自动数据抽取技术。初步实验结果表明所得的抽取规则能从多数搜索引擎上获得较高的抽取率。该方法对于从搜索引擎的搜索结果等结构化、半结构化网页中自动抽取重复模式具有较好的效果。     

RE-OEM：一种半结构化生物数据的信息抽取模型*

在分析半结构化生物数据特点的基础上,提出了一种新的半结构化数据抽取模型RE-OEM。它将OEM数据模型和正则表达式有机地结合起来,不但能够灵活方便地表示各种数据结构,而且能够非常方便地进行模式匹配和数据的定位,为半结构化生物数据的抽取打下坚实基础。     

基于模式匹配的结构化信息抽取

针对半结构化文本的信息抽取粒度较大,不能对抽取结果进行有效语义分析的问题,面向领域提出一种基于模式匹配的结构化信息二次抽取方法.该方法以Web文档形式呈现的半结构化文本为对象,对粗粒度抽取结果进行领域识别,根据识别结果加载相应领域词库.根据模式中各个角色的词性实现模式角色到分词序列词语的映射,从分词序列中抽取出结构化信息,为准确的语义分析提供支持.实验表明该方法能获得更准确的抽取结果.     

半结构化数据的动态树存储模型研究

为了克服半结构化数据存储的不确定性,基于半结构化数据的结构信息可由其模式来描述的思想,提出一种动态树存储模型。在对对象交换模型OEM(Object Exchange Model)进行深度优先遍历,找出所有最大简单路径表达式基础上,采用累加计数原则将得到的最大路径表达式依次添加到一个动态树中,从而生成存储模型。最后将此模型映射到关系表中,实现了半结构化数据在关系数据库中的存储与查询。以村镇土地审批处理系统为实例来说明这种存储模型的有效性。     

Extracting Schema from an OEM Database

While the schema-less feature of the OEM(Object Exchange Modl)gives flexibility in representing semi-structured data,it brings difficulty in formulating database queries. Extracting schema from an OEM database then becomes an important research topic.This paper presents a new approach to this topic with th following reatures.(1)In addition to representing th nested label structure of an OEM database,the proposed OEM schema keeps up-tp-date information about instance objects of the database,The object-level information is useful in speeding up query evaluation.(2)The OEM schema is explicitly represented as a label-set,which is easy to construct and update.(3)The OEM schema of a database is statically built and dynamically updated.The time complexity of building the OEM schems is linear in the size of the OEM database.(4)The approach is applicable to a wide range of areas where the underlying schema is much smaller than the database itself(e.g.data warehouses that are made from a set of heterogeneous databases).     

带冗余的可拆分XML数据树存储与查询研究

XML正在迅速成为WWW上采用的信息交换、表示和存储手段之一。本文首先基于OEM数据模型提出了离散的XML数据模式概念，并以形式化的方式表达了这一思想，以此为出发点给出了带冗余的可拆分XML数据树存储方法，定义了基于模式匹配的数据查询概念，最后给出了以本文方法与传统方法所存储数据查询效率的比较。     

A runtime approach to model-generic translation of schema and data

To support heterogeneity is a major requirement in current approaches to integration and transformation of data. This paper proposes a new approach to the translation of schema and data from one data model to another, and we illustrate its implementation in the tool MIDST-RT.We leverage on our previous work on MIDST, a platform conceived to perform translations in an off-line fashion. In such an approach, the source database (both schema and data) is imported into a repository, where it is stored in a universal model. Then, the translation is applied within the tool as a composition of elementary transformation steps, specified as Datalog programs. Finally, the result (again both schema and data) is exported into the operational system.Here we illustrate a new, lightweight approach where the database is not imported. MIDST-RT needs only to know the schema of the source database and the model of the target one, and generates views on the operational system that expose the underlying data according to the corresponding schema in the target model. Views are generated in an almost automatic way, on the basis of the Datalog rules for schema translation.The proposed solution can be applied to different scenarios, which include data and application migration, data interchange, and object-to-relational mapping between applications and databases.     

基于离散模式的XML数据查询的CSP实现

XML成为WWW上采用的信息交换，表示和存储手段之一，文中给出了基于OEM数据模型的XML数据离散模式概念和基于模式匹配的数据查询概念，重点研究了通过将模式匹配转换到约束满足问题（CSP）来具体求解匹配实例的方法，此方法使WWW上基于内容的查询转变为更为精确的基于结构的查询，理论上证明了这种模式匹配到CSP转换的可行性。     

XML数据查询的CSP实现和算法改进

XML犤1犦正在迅速成为WWW上采用的信息交换、表示和存储手段之一,该文简单介绍了基于OEM数据模型犤2犦的离散XML数据模式概念和在此思想上发展起来的基于模式匹配的数据查询概念;通过将模式匹配转换到约束满足问题(CSP)来具体求解匹配实例,从而使WWW基于内容的查询转变为更为精确的基于结构的查询;接下来,从理论上证明了这种模式匹配到CSP转换的可行性;最后,讨论了求解CSP的算法的改进问题。     

Extracting local schema from semistructured data based on graph-oriented semantic model

Many modern applications(e-commerce,digital library,etc.)require integrated access to various information sources(from tr5aditional RDBMS to semistructured Web repositories).Extracting schema from semistructured data is a prereuisite to integrated heterogeneous information sources.The traditional method that extracts global schema may require time (and space)to increase exponentially with the number of objects and edges in the source.A new method is presented in this paper.which is about extracting local schema,In this method,the algorithm controls the scale of extracting schema within the “schema diameter“ by examining the semantic distance of the target set and using the Hash class and its path distance operation.This method is very efficient for restraining schema from expanding.The prototype validates the new approach.     

Schema Evolution in Data Warehouses

In this paper, we address the issues related to the evolution and maintenance of data warehousing systems, when underlying data sources change their schema capabilities. These changes can invalidate views at the data warehousing system. We present an approach for dynamically adapting views according to schema changes arising on source relations. This type of maintenance concerns both the schema and the data of the data warehouse. The main issue is to avoid the view recomputation from scratch especially when views are defined from multiple sources. The data of the data warehouse is used primarily in organizational decision-making and may be strategic. Therefore, the schema of the data warehouse can evolve for modeling new requirements resulting from analysis or data-mining processing. Our approach provides means to support schema evolution of the data warehouse independently of the data sources. Received 20 March 2000 / Revised 5 January 2001 / Accepted in revised form 20 April 2001     

Schema versioning and database conversion techniques for bi-temporal databases

Schema evolution and schema versioning are two techniques used for managing database evolution. Schema evolution keeps only the current version of a schema and database after applying schema changes. Schema versioning creates new schema versions and converts the corresponding data while preserving the old schema versions and data. To provide the most generality, bi-temporal databases can be used to realize schema versioning, since they allow both retroactive and proactive updates to the schema and database. In this paper we first study two proposed database conversion approaches for supporting schema evolution and schema versioning: single table version approach and multiple table version approach. We then propose the partial table version approach to solve the problems encountered in these approaches when applied to bi-temporal databases. This revised version was published online in June 2006 with corrections to the Cover Date.