Creating meaningful data from web logs for improving the impressiveness of a website by using path analysis method

Web usage mining is to analyze web log files to discover user accessing patterns of web pages. In order to effectively manage and report on a website, it is necessary to get feedback about activity on the web servers. The aim of this study is to help the web designer and web administrator to improve the impressiveness of a website by determining occurred link connections on the website. Therefore, web log files are pre-processed and then path analysis technique is used to investigate the URL information concerning access to electronic sources. The proposed methodology is applied to the web log files in the web server of Firat University. The results and findings of this experimental study can be used by the web designer in order to plan the upgrading and enhancement to the website. With the explosive growth of knowledge sources available on the World Wide Web, it has become more important to find the useful information from these huge amounts of data. At the same time, in the number of websites presents a challenging task for web designers to organize the contents of the websites to provide to the needs of web users. The solutions to these problems can be provided by path analysis using web user navigation patterns. In addition, web designers can improve the design and organization of websites based on the obtained solutions (Das, Turkoglu, & Poyraz, 2007; Etzioni, 1996; Gunduz, 2003; Kosala & Blockeel, 2000). As many researchers believe, it was Etzioni who first came up with the term of web mining in his paper. Web mining is described as the use of data mining techniques to automatically discover and extract useful information from the web documents and services (Etzioni, 1996). In general, web mining research can be classified into three categories: web content mining, web structure mining, and web usage mining (Kosala & Blockeel, 2000). While web structure and content mining utilize primary data on the web, web usage mining works on the secondary data such as web server access logs, proxy sever logs, referrer logs, browser logs, error logs, user profiles, registration data, user sessions or transactions, cookies, user queries, and bookmark data (Gunduz, 2003). Through analyzing these log files and documents we can access to interesting usage patterns and information.

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