Location-Aware Information Delivery with ComMotion
HUC '00 Proceedings of the 2nd international symposium on Handheld and Ubiquitous Computing
Using GPS to learn significant locations and predict movement across multiple users
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Extracting places from traces of locations
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Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
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ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
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MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Discovering semantically meaningful places from pervasive RF-beacons
Proceedings of the 11th international conference on Ubiquitous computing
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Place lab: device positioning using radio beacons in the wild
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
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Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones
Checking in or checked in: comparing large-scale manual and automatic location disclosure patterns
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Annotating mobile phone location data with activity purposes using machine learning algorithms
Expert Systems with Applications: An International Journal
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Personal and Ubiquitous Computing
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SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
Distributed and Parallel Databases
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Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia
From big smartphone data to worldwide research: The Mobile Data Challenge
Pervasive and Mobile Computing
A probabilistic approach to mining mobile phone data sequences
Personal and Ubiquitous Computing
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In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from users' real lives. Two levels of clustering are used to obtain places of interest. First, user location points are grouped using a time-based clustering technique which discovers stay points while dealing with missing location data. The second level performs clustering on the stay points to obtain stay regions. A grid-based clustering algorithm has been used for this purpose. To obtain more user location points, a client-server system has been installed on the mobile phones, which is able to obtain location information by integrating GPS, Wifi, GSM and accelerometer sensors, among others. An extensive set of experiments have been performed to show the benefits of using the proposed framework, using data from the real life of 8 users over 5 continuous months of natural phone usage.