Link Prediction in Temporal Mobile Database

The rapid development of wireless and web technologies has allowed the mobile users to request various kinds of services by mobile devices at anytime and anywhere. The services which are provided to the wireless mobile devices (such as PDAs, Cellular Phones, and Laptops) from anywhere, at any time using ISAP (Information Service and Application Provider) are enhanced by mining and prediction of mobile user behaviors. Given a snapshot of a mobile database, can we infer which customers are likely to access given services in the near future We formalize this question as the link prediction problem and develop approaches to link prediction based on measures for analyzing the probability of different service access by each customer. Differentiated mobile behaviors among users and temporal periods are not considered simultaneously in the previous works. User relations and temporal property are used simultaneously in this work. Improving the performance of mobile behavior prediction helps the service provider to improve the quality of service. Here, we propose a novel data mining method, namely sequential mobile access pattern ( SMAP-Mine) that can efficiently discover mobile users sequential movement patterns associated with requested services. CTMSP-Mine (Cluster-based Temporal Mobile Sequential Pattern - Mine) algorithm is used to mine CTMSPs. In CTMSP-Mine requires user clusters, which are constructed by Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by Location-Based Service Alignment (LBS-Alignment) to construct the user groups. The temporal property is used by time segmenting the logs using time intervals. The user cluster information resulting from CO-Smart-CAST and the time segmentation table are provided as input to CTMSP-Mine technique, which creates CTMSPs. The prediction strategy uses the patterns to predict the mobile user behavior in the near future.