Extracting weblog of Siam University for learning user behavior on MapReduce

MapReduce is a framework that allows developers to write applications that rapidly process and analyze large volumes of data in a massively parallel scale. Moreover, a clickstream is a record of a user's activity on the Internet. Using a clickstream analysis we can collect, analyze, and report aggregate data about which pages visitors visit in what order - and which are the result of the succession of mouse clicks each visitor makes. Clickstream analysis can reveal usage patterns leading to a heightened understanding of users' behavior. In this paper, we introduced a novel and efficient web log mining model for web users clustering. In general, our model consists of three main steps; 1) Computing the similarity measure of any path in a web page, 2) Defining the k-mean clustering for group customerID 3) Generating the report based on the Hadoop MapReduce Framework. Consequently, our experiments were run on real world data derived from weblogs of Siam University at Bangkok, Thailand (www.siam.edu).