Exploiting Data Mining Techniques to Support Context-aware M-services in Mobile Web Service Systems

Abstract

The rapid advance of wireless and web technologies enables the provision of rich kinds of mobile services (m-services) for mobile users. The past studies in mobile services focused mainly on the provision of anytime anywhere services. However, this is insufficient for mobile web systems. The context characteristics, such as the user’s location, the user’s preference and the user’s historical behavior, should also be considered in order to provide the users with context-aware services that will benefit the users. In this paper, we propose a framework of intelligent mobile web service system that supports the personalized (preference-aware) and history-aware m-services. In addition, to help the provision of history-aware m-services, we devise a novel data mining method, namely SMAP-Mine, that can discover mobile users’ sequential movement patterns associated with requested services. Through empirical evaluation under various simulation conditions, SMAP-Mine is shown to deliver excellent performance in terms of accuracy, execution efficiency, and scalability. The implementation of this system is also demonstrated to show its practicability.