Data Management in Location-Dependent Information Services
IEEE Pervasive 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
Modeling techniques for large-scale PCS networks
IEEE Communications Magazine
Mining communities of acquainted mobile users on call detail records
Proceedings of the 2007 ACM symposium on Applied computing
A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
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In this paper, by exploiting the log of call detail records, we present a solution procedure of mining user moving patterns in a mobile computing system. Specifically, we propose algorithm LS to accurately determine similar moving sequences from the log of call detail records so as to obtain moving behaviors of users. By exploring the feature of spatial-temporal locality, we develop algorithm TC to group call detail records into clusters. In light of the concept of regression, we devise algorithm MF to derive moving functions of moving behaviors. Performance of the proposed solution procedure is analyzed and sensitivity analysis on several design parameters is conducted. It is shown by our simulation results that user moving patterns obtained by our solution procedure are of very high quality and in fact very close to real user moving behaviors.