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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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ACM Transactions on Database Systems (TODS)
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Mobile Networks and Applications
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Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Dynamic and Adaptive Cache Retrieval Scheme for Mobile Computing
COOPIS '98 Proceedings of the 3rd IFCIS International Conference on Cooperative Information Systems
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Computer Communications
Modeling techniques for large-scale PCS networks
IEEE Communications Magazine
An efficient method for mining associated service patterns in mobile web environments
Proceedings of the 2003 ACM symposium on Applied computing
Shared Data Allocation in a Mobile Computing System: Exploring Local and Global Optimization
IEEE Transactions on Parallel and Distributed Systems
Global data allocation based on user behaviors in mobile computing environments
Computer Communications
Efficient mining and prediction of user behavior patterns in mobile web systems
Information and Software Technology
Semantic caching for web-based spatial applications
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
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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.