International Journal of Engineering and Technology

In this paper an improved approach for pre processing of web logs data has been proposed and evaluated so that it can be applied for web logs of e-commerce web sites. The resultant web log data after these pre processing steps can be used for further pattern discovery and analysis that helps to provide useful prediction to enhance e-commerce. Ideally, the input for the Web Usage Mining process is a user session file that gives an exact account of who accessed the web site, what pages were requested and in what order, and how long each page was viewed. A user session is the set of the page accesses that occur during a single visit to a web site by a web user. However, the information contained in a raw web server log does not reliably represent a user session file before data pre processing. Hence, data pre processing plays an important role in web usage mining applications. The data preparation process is often the most time consuming and computationally intensive step in the web usage mining process. The scope of this work is to enhance existing pre processing techniques for user and session identification that makes the web log data ready to use. This research work proposes a time-oriented and web ontology based user session identification algorithm which is found to be effective than the existing pre-processing approaches considering the run time, memory usage and processing complexity factors.