The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data: location data from mobile phones, GPS tracks from mobile devices receiving geo-positions from satellites, etc. The GeoPKDD project, a large European research initiative, has studied how to discover useful knowledge about human movement behavior from mobility data, while preserving the privacy of the people under observation. A new exciting multidisciplinary research area has thus started, at the crossroads of mobility, data mining, and privacy.
%0 Journal Article
%1 r.2009mining
%A Giannotti, Fosca
%A Nanni, Mirco
%A Pedreschi, Dino
%A Renso, Chiara
%A Trasarti, Roberto
%D 2009
%J Computational Science and Engineering, 2009. CSE '09. International Conference on
%K modap
%P 948 - 951
%T Mining Mobility Behavior from Trajectory Data
%U http://www2.informatik.hu-berlin.de/~ibach/ISAS_Ibach_Horbank_ueberarbeitet.pdf
%V 4
%X The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data: location data from mobile phones, GPS tracks from mobile devices receiving geo-positions from satellites, etc. The GeoPKDD project, a large European research initiative, has studied how to discover useful knowledge about human movement behavior from mobility data, while preserving the privacy of the people under observation. A new exciting multidisciplinary research area has thus started, at the crossroads of mobility, data mining, and privacy.
@article{r.2009mining,
abstract = {The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data: location data from mobile phones, GPS tracks from mobile devices receiving geo-positions from satellites, etc. The GeoPKDD project, a large European research initiative, has studied how to discover useful knowledge about human movement behavior from mobility data, while preserving the privacy of the people under observation. A new exciting multidisciplinary research area has thus started, at the crossroads of mobility, data mining, and privacy.
},
added-at = {2010-10-08T14:03:36.000+0200},
author = {Giannotti, Fosca and Nanni, Mirco and Pedreschi, Dino and Renso, Chiara and Trasarti, Roberto},
biburl = {https://www.bibsonomy.org/bibtex/2905cbc18ad72885fa0782afa80ae289c/leyli},
interhash = {bc4ea1edd08fb58ee66912bf65006500},
intrahash = {905cbc18ad72885fa0782afa80ae289c},
journal = {Computational Science and Engineering, 2009. CSE '09. International Conference on},
keywords = {modap},
month = {29-31 Aug. 2009 },
pages = {948 - 951},
timestamp = {2010-10-08T14:03:36.000+0200},
title = {Mining Mobility Behavior from Trajectory Data},
url = {http://www2.informatik.hu-berlin.de/~ibach/ISAS_Ibach_Horbank_ueberarbeitet.pdf},
volume = 4,
year = 2009
}