Mining user moving patterns for personal data allocation in a mobile computing system

In this paper, we devise a new data mining algorithm which involves mining for user moving patterns in a mobile computing environment, and utilize the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we devise an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes for proper allocation of personal data. Two personal data allocation schemes, which explore different levels of mining results, are devised: one utilizes the set level of moving patterns and the other utilizes the path level of moving patterns. Performance of these data allocation schemes is comparatively analyzed. It is shown by our simulation results that the user moving patterns is very important in devising effective data allocation schemes which can lead to significant performance improvement in a mobile computing system.