基于编辑距离的XML文档结构聚类的改进算法

针对当前XML文档结构聚类算法的一些不足,指出XML文档树中节点的重复和嵌套影响聚类的质量和效率.利用重复剪枝和嵌套剪枝简化XML文档树的表示,然后根据化简后的结构计算两棵XML文档树中的编辑距离,在此基础上得出两棵树整体的结构相似度量,按照层次聚类方法得到聚类结果.实验证明该算法有比较高的查全率和查准率,有效降低了时间复杂性,具有改进效果.     

IRST(k,l)-Index:一种支持分支路径查询的高效XML结构索引

为快速准确地查询图结构XML文档,本文在互关联后继树(IRST)的基础上,引入结构索引的相似性归并思想,提出一种基于互关联后继树且支持分支路径查询的高效XML结构索引-IRST(k,l)-index,并给出该索引的快速创建和查询算法.经实验验证,与国际上同类索引相比,该索引的创建速度更快、查询效率更高、空间开销更小.     

XML结构聚类

针对当前XML文档结构聚类算法的一些不足，提出采用段匹配的概念来计算两棵XML文档树中的路径相似性，并在此基础上得出两棵树整体的相似度量。在整个聚类过程中，算法还把一组相关文档与一个XML聚类代表相关联，该聚类代表就包含了一个文档集合中所有文档的最相关的特征。为了构建聚类代表，算法通过构造最佳匹配树，合并树，修剪树三步来实现。通过比较聚类代表，发现新的聚类时更新聚类代表来完成文档聚类。实验结果就充分展现了算法的有效性。     

基于编辑图的XML文档相似性研究

目前关于XML文档相似性算法有很多种,其中基于编辑距离的方法是很重要的一类。目前已发表的基于编辑距离的算法中,编辑图算法由于其计算高效率的特点成为研究的出发点。首先介绍了编辑图算法的思想,由于它在计算过程中对同层兄弟节点的顺序有很强的依赖性,因此不能准确有效地比较数据无序的数据中心的XML文档相似性。针对该问题,在编辑图算法思想的基础上,结合路径算法的思想提出拆分编辑图算法。实验结果表明,拆分编辑图算法降低了编辑图算法中对兄弟节点次序的依赖性,更适合于数据中心的XML文档相似性比较,而且所得结果更加准确有效。     

一种基于模糊技术的分类方法

提出一种比较XML文档这种半结构化数据流的模糊技术,并在此基础之上进行分类,主要包括基于结构的文档分类以及基于内容的文档分类。该方法建立在XML文档片段的平面编码基础之上,将XML文档表示成模糊包的形式,使用比较函数,计算出它们结构的相似性。在对XML文档进行基于结构的分类以后,可以进一步考虑其内容,以获得更细的分类。     

XML文档到关系数据库的转换研究

XML作为网络数据交换的标准技术,广泛应用于计算机软件.目前存储数据的主流手段是关系数据库,因此XML文档与关系数据库之间必须进行转换.通过分析XML文档的层次结构,建立了XML文档树模型,并给出结点定义.依据XML的BNF规则给出了元素与属性的正规表达式和相对应的状态转换图,设计了识别元素和属性的词法分析程序用于解析XML文档.提出了XML文档树到关系数据库存储的转换思想和算法,并结合实例给出转换后的关系表.     

基于三叉链表的XML文档存储模型

XML文档进行高效编码、索引、查询的前提是数据的存储模型.针对XML的典型树状结构,文章提出一种基于三叉链表的XML文档存储模型.在此基础上,讨论了XML数据的查询、更新、插入、删除、结点关系判断等运算的实现,分析了相关算法的效率.     

基于文档树的XML文件转换

随着互联网与XML技术的不断发展，实现XML文件与非结构化的文本文件之间的相互转换的要求日趋提高，针对该问题，文章提出了一种基于文档树的XML文件转换方法。该方法通过文档树的形式描述文本文件的结构与内容，在特定的映射规则下对文档树进行遍历以实现RTF文件为代表的文本文件与XML文件的相互转换，最后介绍了文档树的构造及相关算法.     

XML文档的相似测度和结构索引研究

提出了一个可用于定量度量XML文档间差异的方法(称为XED距离)。利用结点间的模拟关系，一个XML文档可以表示为一棵精简的、带权重的结构索引树，两个XML文档间的相似度可以通过计算它们的索引树间的编辑距离来测定，利用索引树可以大大提高判定两个XML文档结构相似度的效率，XED距离测度可用于XML文档的结构搜索、XML文档聚类、XML文档结构抽取、XML文档的变换检测以及XML视图的增量计算和维护等。     

加密XML数据结构索引方案研究

充分利用XML数据库文档的树形结构特性,结合Dewey编码原理和B+树的索引特性,提出了一种基于B+树的加密XML结构索引和查询模型.在XML文档加密过程中,将XML加密数据与基于加密数据的B+树索引一起存储在服务器端,以便在服务器端完成对加密数据的结构索引.实验结果表明,此法提高了查询的效率,无需解密无关的加密数据,有效地实现了对加密XML数据的结构索引.     

XML文档相似性的仿真研究

XML文档相似性的计算是XML文档分类中的一个难题。文中描述了一种基于结构的方法，通过序列化模式挖掘方法，挖掘出两个文档之间的最大相似路径，从而可以通过计算最大相似的路径的节点数目和所有路径的节点数目的比值，得到两个文档之间的相似度。文章提出了一种新的最小化XML文档的方法，并且综合考虑了文档节点的语义相似度和结构相似度，从而进一步地提高了计算文档相似度的精度。实验表明，该方法有着良好的应用前景。     

Fast and effective clustering of XML data using structural information

This paper presents the incremental clustering algorithm, XML documents Clustering with Level Similarity (XCLS), that groups the XML documents according to structural similarity. A level structure format is introduced to represent the structure of XML documents for efficient processing. A global criterion function that measures the similarity between the new document and existing clusters is developed. It avoids the need to compute the pair-wise similarity between two individual documents and hence saves a huge amount of computing effort. XCLS is further modified to incorporate the semantic meanings of XML tags for investigating the trade-offs between accuracy and efficiency. The empirical analysis shows that the structural similarity overplays the semantic similarity in the clustering process of the structured data such as XML. The experimental analysis shows that the XCLS method is fast and accurate in clustering the heterogeneous documents by structures.     

Measuring the structural similarity among XML documents and DTDs

Measuring the structural similarity between an XML document and a DTD has many relevant applications that range from document classification and approximate structural queries on XML documents to selective dissemination of XML documents and document protection. The problem is harder than measuring structural similarity among documents, because a DTD can be considered as a generator of documents. Thus, the problem is to evaluate the similarity between a document and a set of documents. An effective structural similarity measure should face different requirements that range from considering the presence and absence of required elements, as well as the structure and level of the missing and extra elements to vocabulary discrepancies due to the use of synonymous or syntactically similar tags. In the paper, starting from these requirements, we provide a definition of the measure and present an algorithm for matching a document against a DTD to obtain their structural similarity. Finally, experimental results to assess the effectiveness of the approach are presented.     

On the use of hierarchical information in sequential mining-based XML document similarity computation

Measuring the structural similarity among XML documents is the task of finding their semantic correspondence and is fundamental to many web-based applications. While there exist several methods to address the problem, the data mining approach seems to be a novel, interesting and promising one. It explores the idea of extracting paths from XML documents, encoding them as sequences and finding the maximal frequent sequences using the sequential pattern mining algorithms. In view of the deficiencies encountered by ignoring the hierarchical information in encoding the paths for mining, a new sequential pattern mining scheme for XML document similarity computation is proposed in this paper. It makes use of a preorder tree representation (PTR) to encode the XML trees paths so that both the semantics of the elements and the hierarchical structure of the document can be taken into account when computing the structural similarity among documents. In addition, it proposes a postprocessing step to reuse the mined patterns to estimate the similarity of unmatched elements so that another metric to qualify the similarity between XML documents can be introduced. Encouraging experimental results were obtained and reported.     

保持数据约束的关系数据库至XML文档的转换

XML已成为Internet上的技术趋势,在保留原有关系数据库的同时发展XML文档是目前的最佳选择,它需要在保持数据依赖约束基础上实现关系数据库与XML文档的转换.这一过程中,模式转换必须先于数据转换,因为现有的关系数据库通常是规范化的,重建XML文档树结构才能实现这一转换.为了达成此目的,首先依据已有的数据依赖约束将规范化的关系联合进一组表格,实现反向规范化,然后将这些联合表格映射为一组DOM,归并成XML文档树,根据用户选择的根结点,以及与它相连的结点形成一个期望的局部文档树,被选的XML文档树又映射为DTD格式的XML模式.这样就可以将联合表映射成一组DOM,并将其归并成单一DOM,最终转换成XML文档.     

A methodology for clustering XML documents by structure

The processing and management of XML data are popular research issues. However, operations based on the structure of XML data have not received strong attention. These operations involve, among others, the grouping of structurally similar XML documents. Such grouping results from the application of clustering methods with distances that estimate the similarity between tree structures. This paper presents a framework for clustering XML documents by structure. Modeling the XML documents as rooted ordered labeled trees, we study the usage of structural distance metrics in hierarchical clustering algorithms to detect groups of structurally similar XML documents. We suggest the usage of structural summaries for trees to improve the performance of the distance calculation and at the same time to maintain or even improve its quality. Our approach is tested using a prototype testbed.     

Converting relational database into XML documents with DOM

The revolution of XML is recognized as the trend of technology on the Internet to researchers as well as practitioners. Companies need to adopt XML technology. With investment in the current relational database systems, they want to develop new XML documents while running existing relational databases on production. They need to reengineer the relational databases into XML documents with constraints preservation. In the process, schema translation must be done before data conversion. Since the existing relational databases are usually normalized, they have to be reconstructed into XML document tree structures. This can be accomplished through denormalization by joining the normalized relations into tables according to their data dependencies constraints. The joined tables are mapped into DOMs, which are then integrated into XML document trees. The user specifies an XML document root with its relevant nodes to form a partitioned XML document tree to meet their requirements. The selected XML document tree is mapped into an XML schema in the form of DTD. We then load joined tables into DOMs, integrate them into a DOM, and transform it into an XML document.     

A weighted common structure based clustering technique for XML documents

XML has recently become very popular as a means of representing semistructured data and as a standard for data exchange over the Web, because of its varied applicability in numerous applications. Therefore, XML documents constitute an important data mining domain. In this paper, we propose a new method of XML document clustering by a global criterion function, considering the weight of common structures. Our approach initially extracts representative structures of frequent patterns from schemaless XML documents using a sequential pattern mining algorithm. Then, we perform clustering of an XML document by the weight of common structures, without a measure of pairwise similarity, assuming that an XML document is a transaction and frequent structures extracted from documents are items of the transaction. We conducted experiments to compare our method with previous methods. The experimental results show the effectiveness of our approach.     

Semantic and structural similarities between XML Schemas for integration of ubiquitous healthcare data

Currently, a lot of recent electronic health records are based on XML documents. In order to integrate these heterogeneous XML medical documents efficiently, studies on finding structure and semantic similarity between XML Schemas have been exploited. The main problem is how to harvest the most appropriate relatedness to combine two schemas as a global XML Schema for reusing and referring purposes. In this paper, we propose the novel resemblance measure that concurrently considers both structural and semantic information of two specific healthcare XML Schemas. Specifically, we introduce new metrics to compute the datatype and cardinality constraint similarities, which improve the quality of the semantic assessment. On the basis of the similarity between each element pair, we put forward an algorithm to calculate the similarity between XML Schema trees. Experimental results lead to the conclusion that our methodology provides better similarity values than the others with regard to the accuracy of semantic and structure similarities.