Developing Data Allocation Schemes by Incremental Mining of User Moving Patterns in a Mobile Computing System

In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two 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. As can be seen later, the former is useful for the allocation of read-intensive data objects, whereas the latter is good for the allocation of update-intensive data objects. The data allocation schemes for shared data, which are able to achieve local optimization and global optimization, are also developed. Performance of these data allocation schemes is comparatively analyzed. It is shown by our simulation results that the knowledge obtained from the user moving patterns is very important in devising effective data allocation schemes which can lead to significant performance improvement in a mobile