XML structural delta mining: Issues and challenges

Recently, there is an increasing research efforts in XML data mining. These research efforts largely assumed that XML documents are static. However, in reality, the documents are rarely static. In this paper, we propose a novel research problem called XML structural delta mining. The objective of XML structural delta mining is to discover knowledge by analyzing structural evolution pattern (also called structural delta) of history of XML documents. Unlike existing approaches, XML structural delta mining focuses on the dynamic and temporal features of XML data. Furthermore, the data source for this novel mining technique is a sequence of historical versions of an XML document rather than a set of snapshot XML documents. Such mining technique can be useful in many applications such as change detection for very large XML documents, efficient XML indexing, XML search engine, etc. Our aim in this paper is not to provide a specific solution to a particular mining problem. Rather, we present the vision of the mining framework and present the issues and challenges for three types of XML structural delta mining: identifying various interesting structures, discovering association rules from structural deltas, and structural change pattern-based classification.     

一种基于改进粒子群优化的XML结构聚类方法

在对XML文档进行数据挖掘时,很多结构语义信息没有被充分考虑进来.为了更好地进行大规模文档集的挖掘,本文首先给出一个新的基于语义和支持度的XML结构模型对每个文档建模.然后根据该模型,提出基于改进粒子群优化的结构聚类方法.实验中,为了增加算法的实用性,将粒子群优化的思想与传统的K均值算法相结合,其优点是能够跳出局部极值.实验结果表明提出的方法在聚类准确性和收敛程度方面都优于传统基于划分的聚类算法.     

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.     

一种挖掘XML文档频繁子树的方法

本文主要研究从由带标签有序树构成的森林中挖掘嵌入式频繁子树,具体做法是：首先对XML文档进行预处理,生成最简结构树SST,然后从SST中挖掘出频繁子树。本文提出了SSTMiner算法,该算法针对TreeMiner算法存在的瓶颈问题,结合当前所处理的SST的结构特点进行改进,进一步提高了算法执行的效率。实验证明,本文提出的方法能够准确高效地
地挖掘出XML文档中的频繁子树。     

面向半结构化数据的Key约束研究

由于传统的Key约束理论难以适应XML的需求，XML Key约束已成为当前的研究热点。本文从XML文档变化检测的问题入手，提出基于多实例的Key约束概念，并以此作为构建高效XML文档变化检测算法的基础。     

XRules: An effective algorithm for structural classification of XML data

XML documents have recently become ubiquitous because of their varied applicability in a number of applications. Classification is an important problem in the data mining domain, but current classification methods for XML documents use IR-based methods in which each document is treated as a bag of words. Such techniques ignore a significant amount of information hidden inside the documents. In this paper we discuss the problem of rule based classification of XML data by using frequent discriminatory substructures within XML documents. Such a technique is more capable of finding the classification characteristics of documents. In addition, the technique can also be extended to cost sensitive classification. We show the effectiveness of the method with respect to other classifiers. We note that the methodology discussed in this paper is applicable to any kind of semi-structured data. Editors: Hendrik Blockeel, David Jensen and Stefan Kramer An erratum to this article is available at .     

XML数据相似度研究

XML数据的大量出现为信息检索、数据挖掘、智能信息处理提供了机遇和挑战,而相似度计算是XML文档检索、挖掘和深层次智能处理的基础,对相似度计算进行研究具有非常重要的意义。在对XML数据特征进行深入分析的基础上,提出了一种递归相似度计算方法,实验结果表明该方法具有较好的效果。     

一种基于结构索引的XML模式匹配方法

XML文档采用了树型的数据模型,对其查询通常是用带有选择谓词的模式树在XML数据中进行匹配.因此,找出XML文档中所有符合模式树结构的元素集,是XML查询处理的核心操作.本文提出了结构索引JoinGuide,并在此基础上提出了一种新的XML模式匹配方法.它使用JoinGuide来对模式树进行预匹配,这样在XML文档上查询时可以利用索引上的匹配结果来忽略部分连接谓词和不必要的候选XML元素序列.本文还提出了三种具体算法来利用索引匹配结果进行进一步的查询.实验结果表明本文中的模式树匹配方法优于以往的匹配方法,并且索引所需的空间很小.     

XML压缩算法的分析与比较

XML为在Web上发布和交换异质数据提供了相当的灵活性。但由于这种语言自身就有冗余的特性所以XML文档在体积都大于有相同数据内容的其他类型的文档。随着XML在web上应用的扩展其数据大小自然也会随之而增加．而这实质上增加了数据的存储量、处理量和交换量，因此XML文档的体积问题也阻碍了XML的应用，特别是阻碍了XML在具有带宽和内存容量限制的应用上如移动通讯应用。在这篇文章中，我们将大致的介绍一下最近提出的几种针对XML的压缩算法并分析它们在解决XML文档体积问题上的技术和效能。     

XML压缩算法的分析与比较

XML为在Web上发布和交换异质数据提供了相当的灵活性。但由于这种语言自身就有冗余的特性所以XML文档在体积都大于有相同数据内容的其他类型的文档。随着XML在Web上应用的扩展其数据大小自然也会随之而增加,而这实质上增加了数据的存储量、处理量和交换量,因此XML文档的体积问题也阻碍了XML的应用,特别是阻碍了XML在具有带宽和内存容量限制的应用上如移动通讯应用。在这篇文章中,我们将大致的介绍一下最近提出的几种针对XML的压缩算法并分析它们在解决XML文档体积问题上的技术和效能。     

基于压缩结构树的XML数据频繁模式挖掘研究

XML文档频繁模式挖掘是XML相关研究工作中的重要内容。在现有的频繁树结构挖掘算法WL的基础上，提出了一种高效的基于压缩结构树存储结构的XML数据频繁模式挖掘算法AFPMX_CST。该算法压缩了搜索空间，减少了扫描次数，相对于WL算法在时间效率和空间效率方面具有更加良好的性能。同时，该文进一步研究了将挖掘结果转换为相应的DTD格式的方法及过程。实验结果表明AFPMX_CST算法是可行和有效的。     

Comparative Analysis of XML Compression Technologies

XML provides flexibility in publishing and exchanging heterogeneous data on the Web. However, the language is by nature verbose and thus XML documents are usually larger in size than other specifications containing the same data content. It is natural to expect that the data size will continue to grow as XML data proliferates on the Web. The size problem of XML documents hinders the applications of XML, since it substantially increases the costs of storing, processing and exchanging the data. The hindrance is more apparent in bandwidth- and memory-limited settings such as those applications related to mobile communication. In this paper, we survey a range of recently proposed XML specific compression technologies and study their efforts and capabilities to overcome the size problem. First, by categorizing XML compression technologies into queriable and unqueriable compressors, we explain the efforts in the representative technologies that aim at utilizing the exposed structure information from the input XML documents. Second, we discuss the importance of queriable XML compressors and assess whether the compressed XML documents generated from these technologies are able to support direct querying on XML data. Finally, we present a comparative analysis of the state-of-the-art XML conscious compression technologies in terms of compression ratio, compression and decompression times, memory consumption, and query performance.     

XCQ: A queriable XML compression system

XML has already become the de facto standard for specifying and exchanging data on the Web. However, XML is by nature verbose and thus XML documents are usually large in size, a factor that hinders its practical usage, since it substantially increases the costs of storing, processing, and exchanging data. In order to tackle this problem, many XML-specific compression systems, such as XMill, XGrind, XMLPPM, and Millau, have recently been proposed. However, these systems usually suffer from the following two inadequacies: They either sacrifice performance in terms of compression ratio and execution time in order to support a limited range of queries, or perform full decompression prior to processing queries over compressed documents.In this paper, we address the above problems by exploiting the information provided by a Document Type Definition (DTD) associated with an XML document. We show that a DTD is able to facilitate better compression as well as generate more usable compressed data to support querying. We present the architecture of the XCQ, which is a compression and querying tool for handling XML data. XCQ is based on a novel technique we have developed called DTD Tree and SAX Event Stream Parsing (DSP). The documents compressed by XCQ are stored in Partitioned Path-Based Grouping (PPG) data streams, which are equipped with a Block Statistics Signature (BSS) indexing scheme. The indexed PPG data streams support the processing of XML queries that involve selection and aggregation, without the need for full decompression. In order to study the compression performance of XCQ, we carry out comprehensive experiments over a set of XML benchmark datasets. Wilfred Ng obtained his M.Sc.(Distinction) and Ph.D. degrees from the University of London. His research interests are in the areas of databases and information Systems, which include XML data, database query languages, web data management, and data mining. He is now an assistant professor in the Department of Computer Science, the Hong Kong University of Science and Technology (HKUST). Further Information can be found at the following URL: . Wai-Yeung Lam obtained his M.Phil. degree from the Hong Kong University of Science and Technology (HKUST) in 2003. His research thesis was based on the project “XCQ: A Framework for Querying Compressed XML Data.” He is currently working in industry. Peter Wood received his Ph.D. in Computer Science from the University of Toronto in 1989. He has previously studied at the University of Cape Town, South Africa, obtaining a B.Sc. degree in 1977 and an M.Sc. degree in Computer Science in 1982. Currently he is a senior lecturer at Birkbeck and a member of the Information Management and Web Technologies research group. His research interests include database and XML query languages, query optimisation, active and deductive rule languages, and graph algorithms. Mark Levene received his Ph.D. in Computer Science in 1990 from Birkbeck College, University of London, having previously been awarded a B.Sc. in Computer Science from Auckland University, New Zealand in 1982. He is currently professor of Computer Science at Birkbeck College, where he is a member of the Information Management and Web Technologies research group. His main research interests are Web search and navigation, Web data mining and stochastic models for the evolution of the Web. He has published extensively in the areas of database theory and web technologies, and has recently published a book called ‘An Introduction to Search Engines and Web Navigation’.     

XTREAM: An efficient multi-query evaluation on streaming XML data

Recently, there has been growing interest in streaming XML data. Much of the work on streaming XML data has been focused on efficient filtering. Filtering systems deliver XML documents to interested users. The burden of extracting the XML fragments of interest from XML documents is placed on users. In this paper, we propose XTREAM which evaluates multiple queries in conjunction with the read-once nature of streaming data. In contrast to the previous work, XTREAM supports a wide class of XPath queries including tree shaped expressions, order based predicates, and nested predicates. In addition, to improve the efficiency and scalability of XTREAM, we devise an optimization technique called Query Compaction. Experimental results with real-life and synthetic XML data demonstrate the efficiency and scalability of XTREAM.     

Schema-aware XPath filtering on XML document streams

The XML stream filtering is gaining widespread attention from the research community in recent years. There have been many efforts to improve the performance of the XML filtering system by utilizing XML schema information. In this paper, we design and implement an XML stream filtering system, SFilter, which uses DTD or XML schema information for improving the performance. We propose the simplification and two kinds of optimization, one is static and the other is dynamic optimization. The Simplification and static optimization transform the XPath queries to make automata as an index structure for the filtering. The dynamic optimization are done in runtime at the filtering time. We developed five kinds of static optimization and two kinds of dynamic optimization. We present the novel filtering algorithm for the resulting transformed XPath queries and runtime optimizing. The experimental result shows that our system filters the XML streams efficiently.     

A practical approach to extracting DTD-conforming XML documents from heterogeneous data sources

XML documents are becoming popular for business process integration. To achieve interoperability between applications, XML documents must also conform to various commonly used data type definitions (DTDs). However, most business data are not maintained as XML documents. They are stored in various native formats, such as database tables or LDAP directories. Hence, a middleware is needed to dynamically generate XML documents conforming to predefined DTDs from various data sources. As industrial consortia and large corporations have created various DTDs, it is both challenging and time-consuming to design the necessary middleware to conform to so many different DTDs. This problem is particularly acute for a small- or medium-sized enterprise because it lacks the IT skills to quickly develop such a middleware. In this paper, we present XLE, an XML Lightweight Extractor, as a practical approach to dynamically extracting DTD-conforming XML documents from heterogeneous data sources. XLE is based on a framework called DTD source annotation (DTDSA). It treats a DTD as the control structure of a program. The annotations become the program statements, such as functions and assignments. DTD-conforming XML documents are generated by parsing annotated DTDs. Basically, DTD annotations describe declaratively the mappings between target XML documents and the source data. The XLE engine implements a few basic annotations, providing a practical solution for many small- and medium-sized enterprises. However, XLE is designed to be versatile. It allows sophisticated users to plug in their own implementations to access new types of data or to achieve better performance. Heterogeneous data sources can be simply specified in the annotations. A GUI tool is provided to highlight the places where annotations are needed.     

XML 动态区间编码方法

提出了适用于XML文档更新环境下的区间编码方法——DCLS(dynamic containment labeling scheme).DCLS将基于整数的编码泛化到基于向量的编码,扩展了传统静态区间编码方法,有效避免了XML文档更新时的重新编码.不论文档更新与否,DCLS都显示了良好的性能:DCLS利用基于整数的静态区间编码方法进行初始编码,在文档不更新的环境下,具有较高的存储效率和查询性能;同时,DCLS将整数视为特殊向量,不仅能够支持文档更新,而且更新效率高;特别是倾斜插入时,DCLS可以避免编码位长的快速增加.实验结果表明,与已有的动态区间编码方法相比,DCLS具有更好的性能.     

XN-Store:一种原生XML数据库的存储方案

随着XML相关标准的推广与应用,Web上出现了大量的XML文档,为了进行有效的管理,有必要将XML文档存储到数据库中,存储方案已成为XML数据管理领域研究的一个重要课题,将XML文档映射为关系表,存储到传统的RDBMS中,会破坏XML数据的树形结构,造成查询效率的下降,提出了一种新的用于原生XML数据库的存储方案--XN-Store,该方案基于索引结构将XML节点作为记录直接存储到分页文件中,建立起持久化文档对象模型,从而保持了XML数据原有的树形结构.XN-Store不仅降低了XML文档的存储空间开销,而且实现了XML节点的快速串行化输出和访问操作.作为通用的原生XML存储方案,XN-Store支持各种二级索引的创建,以提高XML查询处理的效率,采用多种数据集,分别在XN-Store和先前的XML存储系统上进行实验,比较存储空间、存储时间、串行化时间和节点访问时间.实验结果表明,XN-Store是一种高性能的原生XML数据库存储方案.     

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。