Mobile computing and databases: anything new?
ACM SIGMOD Record
An adaptive data replication algorithm
ACM Transactions on Database Systems (TODS)
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
Client-server computing in mobile environments
ACM Computing Surveys (CSUR)
Mobile Computing and Databases-A Survey
IEEE Transactions on Knowledge and Data Engineering
Locating Objects in Mobile Computing
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on 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
Shared Data Allocation in a Mobile Computing System: Exploring Local and Global Optimization
IEEE Transactions on Parallel and Distributed Systems
A Data Allocation Scheme using Data Mining for Wireless Cellular Network
CTS '06 Proceedings of the International Symposium on Collaborative Technologies and Systems
Distributed User Modeling for Personalized News Delivery in Mobile Devices
SMAP '07 Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization
A comparative analysis of the user behavior in academic WiFi networks
Proceedings of the 6th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Characterizing User Behavior in a European Academic WiFi Network
International Journal of Handheld Computing Research
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In this paper, we utilized user behaviors to do global data allocation in mobile computing environments. The issue we addressed here is to allocate right data at the right location for whole users in the environment. The user behaviors include user moving patterns and user request patterns. By utilizing the information, we developed four algorithms focusing on different views, respectively, such as hit ratios, communication costs, and response time. According to the data allocation schemes produced from these algorithms, we conducted several experiments to evaluate them and compared their pro and con.