LeZi-update: an information-theoretic approach to track mobile users in PCS networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
A Traveling Salesman Mobility Model and Its Location Tracking in PCS Networks
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Spatio-Temporal Aggregation Using Sketches
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Effective Density Queries on ContinuouslyMoving Objects
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A Dynamic Mobility Histogram Construction Method Based on Markov Chains
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
An integrated space-time pattern classification approach for individuals' travel trajectories
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
A system for destination and future route prediction based on trajectory mining
Pervasive and Mobile Computing
A regression-based approach for mining user movement patterns from random sample data
Data & Knowledge Engineering
A framework of mining semantic regions from trajectories
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Predictability of individuals' mobility with high-resolution positioning data
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A mixed autoregressive hidden-markov-chain model applied to people's movements
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
Distributed and Parallel Databases
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Many studies of spatiotemporal pattern discovery partition data space into disjoint cells for effective processing. However, the discovery accuracy of the space-partitioning schemes highly depends on space granularity. Moreover, it cannot describe data statistics well when data spreads over not only one but many cells. In this study, we introduce a novel approach which takes advantages of the effectiveness of space-partitioning methods but overcomes those problems. Specifically, we uncover frequent regions where an object frequently visits from its trajectories. This process is unaffected by the space-partitioning problems. We then explain the relationships between the frequent regions and the partitioned cells using trajectory pattern models based on hidden Markov process. Under this approach, an object's movements are still described by the partitioned cells, however, its patterns are explained by the frequent regions which are more precise. Our experiments show the proposed method is more effective and accurate than existing spacepartitioning methods.