Self-Organizing Maps
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Finding similar users using category-based location history
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Journal of Location Based Services - GeoVA(t) - Geospatial visual analytics: focus on time. Special issue of the ICA Commission on GeoVisualisation
Recommending Social Events from Mobile Phone Location Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Unveiling the complexity of human mobility by querying and mining massive trajectory data
The VLDB Journal — The International Journal on Very Large Data Bases
Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome
IEEE Transactions on Intelligent Transportation Systems
Semantic enrichment of mobile phone data records
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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The huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.