Introduction to algorithms
Mobile wireless computing: challenges in data management
Communications of the ACM
An adaptive data replication algorithm
ACM Transactions on Database Systems (TODS)
Escrow techniques for mobile sales and inventory applications
Wireless Networks
Per-user profile replication in mobile environments: algorithms, analysis, and simulation results
Mobile Networks and Applications
A mobile transaction model that captures both the data and movement behavior
Mobile Networks and Applications
Efficient and flexible location management techniques for wireless communication systems
Wireless Networks - Special issue: mobile computing and networking: selected papers from MobiCom '96
Client-server computing in mobile environments
ACM Computing Surveys (CSUR)
Emerging mobile and wireless networks
Communications of the ACM
ACM SIGMOBILE Mobile Computing and Communications Review
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mobile Computing and Databases-A Survey
IEEE Transactions on Knowledge and Data Engineering
Performance Modeling of Distributed and Replicated Databases
IEEE Transactions on Knowledge and Data Engineering
Locating Objects in Mobile Computing
IEEE Transactions on Knowledge and Data Engineering
Mining User Moving Patterns for Personal Data Allocation in a Mobile Computing System
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Database Replication for the Mobile Era
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Supporting collaborative applications in a heterogeneous mobile environment
Computer Communications
UMTS/IMT-2000 based on wideband CDMA
IEEE Communications Magazine
Evolution of wireless data services: IS-95 to cdma2000
IEEE Communications Magazine
Wideband code division multiple access
IEEE Journal on Selected Areas in Communications
An efficient location and routing scheme for mobile computing environments
IEEE Journal on Selected Areas in Communications
Global data allocation based on user behaviors in mobile computing environments
Computer Communications
A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
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In this paper, we devise data allocation algorithms that can utilize the knowledge of user moving patterns for proper allocation of shared data in a mobile computing system. By employing the data allocation algorithms devised, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. The data allocation algorithms for shared data, which are able to achieve local optimization and global optimization, are developed. Local optimization refers to the optimization that the likelihood of local data access by an individual mobile user is maximized whereas global optimization refers to the optimization that the likelihood of local data access by all mobile users is maximized. Specifically, by exploring the features of local optimization and global optimization, we devise algorithm SD-local and algorithm SD-global to achieve local optimization and global optimization, respectively. In general, the mobile users are divided into two types, namely, frequently moving users and infrequently moving users. A measurement, called closeness measure which corresponds to the amount of the intersection between the set of frequently moving user patterns and that of infrequently moving user patterns, is derived to assess the quality of solutions provided by SD-local and SD-global. Performance of these data allocation algorithms is comparatively analyzed. From the analysis of SD-local and SD-global, it is shown that SD-local favors infrequently moving users whereas SD-global is good for frequently moving users. The simulation results show that the knowledge obtained from the user moving patterns is very important in devising effective data allocation algorithms which can lead to prominent performance improvement in a mobile computing system.