Developing data allocation schemes by incremental mining of user moving patterns in a mobile computing system

• Published in: IEEE transactions on knowledge and data engineering 2003-01, Vol.15 (1), p.70-85
• Main Authors: Peng, W.-C., Chen, M.-S.
• Format: Article
• Language: English
• Subjects: Algorithms; Allocations; Computation; Computational modeling; Computer architecture; Computing costs; Cost engineering; Costs; Data mining; Distributed computing; Information systems; Mining; Mobile communication; Mobile communication systems; Mobile communications networks; Mobile computing; Performance analysis; Performance enhancement; Studies; Wireless communication
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2003.1161583

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 schemes are devised: one utilizes the set level of moving patterns and the other utilizes their path level. Schemes for shared data are also developed. Performance of these schemes is comparatively analyzed.