OPWUMP: An Architecture for Online Predicting in WUM-Based Personalization System

Abstract

The Internet is one of the fastest growing areas of intelligence gathering. During their navigation web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Sophisticated mining processes are needed for this knowledge to be extracted, understood and used. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web Site. WUM can model user behavior and, therefore, to forecast their future movements. Online prediction is one web usage mining application. However, the accuracy of the prediction and classification in the current architecture of predicting users’ future requests systems can not still satisfy users especially in Huge Web sites. To provide online prediction efficiently, we develop an architecture for online predicting in WUM-based personalization system (OPWUMP).This article advances an architecture of Web usage mining for enhancing accuracy of classification by interaction between classification, evaluation, current user activates and user profile in online phase of this architecture.