PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Building realistic mobility models from coarse-grained traces
Proceedings of the 4th international conference on Mobile systems, applications and services
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
BreadCrumbs: forecasting mobile connectivity
Proceedings of the 14th ACM international conference on Mobile computing and networking
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining correlation between locations using human location history
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Prediction of moving object location based on frequent trajectories
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Predicting user's movement with a combination of self-organizing map and markov model
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Pedestrian-movement prediction based on mixed Markov-chain model
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Mobility data is Big Data. Modeling such raw big location data is quite challenging in terms of quality and runtime efficiency. Mobility data emanating from smart phones and other pervasive devices consists of a combination of spatio-temporal dimensions, as well as some additional contextual dimensions that may range from social network activities, diseases to telephone calls. However, most existing trajectory models focus only on the spatio-temporal dimensions of mobility data and their regions of interest depict only the popularity of a place. In this paper, we propose a novel trajectory model called Time Mobility Context Correlation Pattern (TMC-Pattern), which considers a wide variety of dimensions and utilizes subspace clustering to find contextual regions of interest. In addition, our proposed TMC-Pattern rigorously captures and embeds infrastructural, human, social and behavioral patterns into the trajectory model. We show theoretically and experimentally, how TMC-Pattern can be used for Frequent Location Sequence Mining and Location Prediction with real datasets.