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  1. Proceedings of the Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems (MobiGIS '13)
  2. Predicting the change of location of mobile phone users
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Trajectory pattern change analysis in campus WiFi networks
Nearest neighbor queries with service time constraints in time-dependent road networks
Predicting the change of location of mobile phone users
An architecture for surroundings discovery by linking 3D models and LOD cloud
Geographical and temporal similarity measurement in location-based social networks
Extending battery lifetime of mobile devices with geofence capabilities on dynamic-speed urban environments
Understanding population displacements on location-based call records using road data
osmfind: fast textual search on OSM data -- on smartphones and servers
PLATIS: a personalized location-aware tourist information system

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Predicting the change of location of mobile phone users

Content Provider ACM Digital Library
Author Keles, Ilkcan Ozer, Mert Karagoz, Pinar Toroslu, İsmail Hakki
Abstract Predicting the location of people from their mobile phone logs is becoming an attractive research area. Due to two main reasons this problem is very challenging: the log data is very large and there is a variety of granularity levels both for specifying the location and the time, especially with low granularity level it becomes much more complicated to define common user behaviour patterns. In this work, rather than determining the next location of a person, we focus on the predicting the location of a person when it changes. We employed a two phase method; which first clusters the data to obtain a higher granularity level, and then extracts frequent sequential patterns corresponding to location changes. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results in predicting the change of location of mobile phone users.
Starting Page 43
Ending Page 50
Page Count 8
File Format PDF
ISBN 9781450325318
DOI 10.1145/2534190.2534196
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-05
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Mobile phone users Location prediction
Content Type Text
Resource Type Article
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