Querying geo-social data by bridging spatial networks and social networks
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
Discovering human places of interest from multimodal mobile phone data
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
A personal route prediction system based on trajectory data mining
Information Sciences: an International Journal
Mining individual mobility patterns from mobile phone data
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
Storing routes in socio-spatial networks and supporting social-based route recommendation
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Semantic trajectory mining for location prediction
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Querying socio-spatial networks on the world-wide web
Proceedings of the 21st international conference companion on World Wide Web
The preface of the 4th International Workshop on Location-Based Social Networks
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
When and where next: individual mobility prediction
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Finding frequent sub-trajectories with time constraints
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
Incremental Frequent Route Based Trajectory Prediction
Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
Mining geographic-temporal-semantic patterns in trajectories for location prediction
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Hi-index | 0.00 |
The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enables people to conveniently log their location history into spatial-temporal data, thus giving rise to the necessity as well as opportunity to discovery valuable knowledge from this type of data. In this paper, we propose the novel notion of individual life pattern, which captures individual's general life style and regularity. Concretely, we propose the life pattern normal form (the LP-normal form) to formally describe which kind of life regularity can be discovered from location history; then we propose the LP-Mine framework to effectively retrieve life patterns from raw individual GPS data. Our definition of life pattern focuses on significant places of individual life and considers diverse properties to combine the significant places. LP-Mine is comprised of two phases: the modelling phase and the mining phase. The modelling phase pre-processes GPS data into an available format as the input of the mining phase. The mining phase applies separate strategies to discover different types of pattern. Finally, we conduct extensive experiments using GPS data collected by volunteers in the real world to verify the effectiveness of the framework.