LeZi-update: an information-theoretic framework for personal mobility tracking in PCS networks
Wireless Networks - Selected Papers from Mobicom'99
Introduction to Algorithms
Data Management in Location-Dependent Information Services
IEEE Pervasive Computing
IEEE Transactions on Knowledge and Data Engineering
An efficient method for mining associated service patterns in mobile web environments
Proceedings of the 2003 ACM symposium on Applied computing
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Shared Data Allocation in a Mobile Computing System: Exploring Local and Global Optimization
IEEE Transactions on Parallel and Distributed Systems
Wireless and Mobile All-IP Networks
Wireless and Mobile All-IP Networks
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Finding Fastest Paths on A Road Network with Speed Patterns
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
DTTC: Delay-Tolerant Trajectory Compression for Object Tracking Sensor Networks
SUTC '06 Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing -Vol 1 (SUTC'06) - Volume 01
Efficient mining of group patterns from user movement data
Data & Knowledge Engineering
Location query based on moving behaviors
Information Systems
k-STARs: Sequences of Spatio-Temporal Association Rules
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive fastest path computation on a road network: a traffic mining approach
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A conceptual view on trajectories
Data & Knowledge Engineering
CarWeb: A Traffic Data Collection Platform
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Mining closed patterns in multi-sequence time-series databases
Data & Knowledge Engineering
Clustering object moving patterns for prediction-based object tracking sensor networks
Proceedings of the 18th ACM conference on Information and knowledge management
Exploring regression for mining user moving patterns in a mobile computing system
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Mining multilevel and location-aware service patterns in mobile web environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling techniques for large-scale PCS networks
IEEE Communications Magazine
Mining trajectory patterns using hidden Markov models
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Stream mining on univariate uncertain data
Applied Intelligence
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Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users.