MINING USER MOVEMENT BEHAVIOR PATTERNS IN A MOBILE SERVICE ENVIRONMENT

ABSTRACT Mobile service systems offer mobile users useful information ubiquitously via mobile devices. Based on changeable user movement behavior patterns (UMBPs), mobile service systems have the capability of effectively mining a special request from abundant data. In this paper, UMBPs are studied in terms of the problem of mining matching mobile access patterns based on joining the following four kinds of characteristics, U, L, T, and S, where U is the mobile user, L is the movement location, T is the dwell time in the timestamp and S is the service request. By introducing standard graph-matching algorithms along with the primitives of a database management system, which comprises grouping, sorting and joining, these joint operations are defined. Moreover, by mining the associated structure via maximum weight bipartite graph matching, a prediction mechanism, based on the model of UMBPs, is utilized to find strong relationships among U, L, T and S. Usage of Weblogs is very limited and it is based on their location in the existing system; but, it is tracked on service usage in the proposed system. Match Joins using Max Flows are used in the new system which helps to compress the input relations by using group-by operation leads conditions, using synthetically generated , is introduced. Finally, performance studies are conducted to show that, in terms of execution efficiency and scalability, the proposed procedures produced excellent performance results. Keywords: U, L, T,S UMBPS, mobile services, mobility prediction, mobile access patterns, Mobile Access Pattern Matching.