Mining XML Documents

Abstract

XML documents are becoming ubiquitous because of their rich and flexible format that can be used for a variety of applications. Giving the increasing size of XML collections as information sources, mining techniques that traditionally exist for text collections or databases need to be adapted and new methods to be invented to exploit the particular structure of XML documents. Basically XML documents can be seen as trees, which are well known to be complex structures. This chapter describes various ways of using and simplifying this tree structure to model documents and support efficient mining algorithms. We focus on three mining tasks: classification and clustering which are standard for text collections; discovering of frequent tree structure which is especially important for heterogeneous collection. This chapter presents some recent approaches and algorithms to support these tasks together with experimental evaluation on a variety of large XML collections

Enabling efficient business process mining using flatten sequential structure model

“…One of the reasons these business process log data store in this semi-structured format is the capability of XML format to represent the contextual information among different attribute or metadata in a domain unambiguous method. Nevertheless, it is quite challenging to perform statistical analysis and data mining technique to XML data because of the complex data structure and dimensions (structure dimension and content dimension) [19]. Due to the similar characteristic of XML document and trees structured data, many researchers modelled XML document as an ordered, labelled and rooted trees.…”