EXTRACTING INTERESTING KNOWLEDGE FROM VERSIONS OF DYNAMIC XML DOCUMENTS

Abstract

XML has became very popular for representing semi structured data and a standard for data exchange over the web these days. The data exchanged as XML is growing continuously, so the necessity to not only store these large volumes of XML data for future use, but to mine them to discover interesting information has became obvious. The extracted knowledge can be used to make predictions. Recently, a large amount of work has been done in XML data mining. Most of the existing work focuses on the static XML data mining, while XML data is dynamic in real applications. So research has been focused more on XML documents versions and extracting information from versions of XML documents. Approach proposed in this paper is for mining association rules from changes of versions of dynamic XML documents by using information present in the consolidated delta which can be used for making future predictions.