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Discovering Places of Interest in Everyday Life from Smartphone Data
| Citations: | 7 - 0 self |
BibTeX
@MISC{Montoliu_discoveringplaces,
author = {Raul Montoliu and Jan Blom and Daniel Gatica-perez},
title = { Discovering Places of Interest in Everyday Life from Smartphone Data},
year = {}
}
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Abstract
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. A place-of-interest is defined as a location where the user usually goes and stays for a while. 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 has been performed to show the benefits of using the proposed framework, using data from the real life of a significant number of users over almost a year of natural phone usage.
Keyphrases
everyday life discovering place location information mobile phone user location point grid-based clustering algorithm significant number extensive set accelerometer sensor natural phone usage time-based clustering technique stay region stay point real life user real life second level performs client-server system location data phone data new framework
