A lot of applications including event logs and web pages uses XML format for utilizing, keeping, transferring and displaying data. Thus, volume of data expressed in XML has increase rapidly. Numerous research has been done to extract and mine information from XML documents. Mining XML documents allows an understanding to the architecture and composition of XML documents. Generally, frequent subtree mining is one of the methods to mine XML documents. Frequent subtree mining searches the relation between data in a tree structured database. Due to the architecture and the composition of XML format, normal data mining and statistical analysis difficult to be performed. This paper suggests a framework that flattens and converts tree structured data into structured data, while maintaining the information of architecture and the composition of XML format. To gain more information from event logs, converting into structured data from semistructured format grants more ability to perform variety data mining techniques and statistical test. Keywords: Flatten Sequential Structure Model, XML Format Event Logs, Data Mining, Statistical Analysis.