Data Mining on XML Data

Data Mining on XML Data

Qin Ding (East Carolina University, USA)
Copyright: © 2009 |Pages: 5
DOI: 10.4018/978-1-60566-010-3.ch079
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With the growing usage of XML data for data storage and exchange, there is an imminent need to develop efficient algorithms to perform data mining on semistructured XML data. Mining on XML data is much more difficult than mining on relational data because of the complexity of structure in XML data. A naïve approach to mining on XML data is to first convert XML data into relational format. However the structure information may be lost during the conversion. It is desired to develop efficient and effective data mining algorithms that can be directly applied on XML data.
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In recent years, XML has become very popular for representing semi-structured data and a standard for data exchange over the web. XML stands for Extensible Markup Language. It is a simple and very flexible text format derived from SGML (Standard Generalized Markup Language). Both XML and SGML are meta-languages because they are used for defining markup language. Originally designed to meet the challenges of large-scale electronic publishing, XML is also playing an increasingly important role in the exchange of a wide variety of data on the Web and elsewhere.

Below is a simplified example of an XML document. As can be seen, elements (or called tags) are the primary building blocks of an XML document. Unlike HTML which uses a fixed set of tags, XML allows user to define new collections of tags that can be used to structure any type of data or document. An element can have descriptive attributes that provide additional information about the element. Document Type Definitions (DTDs) can be used to specify which elements and attributes we can use and the constraints on these elements, such as how elements can be nested. XML allows the representation of semi-structured and hierarchal data containing not only the values of individual items but also the relationships between data items.

Data mining is the process to extract useful patterns or knowledge from large amount of data. As the amount of available XML data is growing continuously, it will be interesting to perform data mining on XML data so that useful patterns can be extracted from XML data. From the example in Figure 1, we might be able to discover such patterns as “researchers who published about XML also published something related to XQuery” where “XML” and “XQuery” are keywords of the publications. This interesting pattern can be represented as an association rule in the format of “XML => XQuery”. The task of data mining on XML data can be significant, yet challenging, due to the complexity of the structure in XML data.

Figure 1.

An XML document

Data mining has been successfully applied to many areas, ranging from business, engineering, to bioinformatics (Han & Kamber, 2006). However, most data mining techniques were developed for data in relational format. In order to apply these techniques to XML data, normally we need to first convert XML data into relational data format, and then traditional data mining algorithms can be applied on converted data. One way to map XML data into relational schema is to decompose XML documents entirely, remove the XML tags, and store the element and attribute values in relational tables. In this process, most aspects of the XML document structure are usually lost, such as the relative order of elements in the document, and the nested structure of the elements.

In order to avoid mapping the XML data to relational format, researchers have been trying to utilize XQuery, the W3C (World Wide Web Consortium) standard query language for XML, to support XML mining. On the one hand, XQuery provides a flexible way to extract XML data; on the other hand, by adding another layer using XQuery, the mining efficiency may be greatly affected. In addition, a query language such as XQuery may have limited querying capabilities to support all the data mining functionalities.

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