research-article

Predicting human behaviour from selected mobile phone data points

Authors:

Driss Choujaa,

Naranker DulayAuthors Info & Claims

UbiComp '10: Proceedings of the 12th ACM international conference on Ubiquitous computing

Pages 105 - 108

https://doi.org/10.1145/1864349.1864368

Published: 26 September 2010 Publication History

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Abstract

The mobile phone offers a unique opportunity to predict a person's behaviour automatically for advanced ubiquitous services. In this note, we analyse cellular data collected as part of the Reality Mining project and use information-theoretic concepts to answer three questions (i) What time points in the day help predict a mobile phone user's activity at another time point? (ii) What time points in history are most useful to predict his future activities? and (iii) How difficult is it to predict his activity at a given time from another user's activity at another time?

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Cited By

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Kong XMa KHou SShang DXia F(2019)Human Interactive Behavior: A Bibliographic ReviewIEEE Access10.1109/ACCESS.2018.28873417(4611-4628)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2887341
Toader BCantelmo GPopescu MViti F(2018)Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis2018 21st International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2018.8569935(993-998)Online publication date: Nov-2018
https://doi.org/10.1109/ITSC.2018.8569935
Lugano G(2018)Assessing Individual and Group Behavior from Mobility Data: Technological Advances and Emerging ApplicationsEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_219(93-101)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_219
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Index Terms

Predicting human behaviour from selected mobile phone data points
1. Mathematics of computing
  1. Information theory

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Information

Published In

UbiComp '10: Proceedings of the 12th ACM international conference on Ubiquitous computing

September 2010

366 pages

ISBN:9781605588438

DOI:10.1145/1864349

General Chairs:
Jakob E. Bardram
IT University of Copenhagen, Denmark
,
Marc Langheinrich
University of Lugano, Switzerland
,
Program Chairs:
Khai N. Truong
University of Toronto, Canada
,
Paddy Nixon
University College Dublin, Ireland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Florida: University of Florida

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2010

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Ubicomp '10

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Ubicomp '10: The 2010 ACM Conference on Ubiquitous Computing

September 26 - 29, 2010

Copenhagen, Denmark

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UbiComp '10 Paper Acceptance Rate 39 of 202 submissions, 19%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all

Kong XMa KHou SShang DXia F(2019)Human Interactive Behavior: A Bibliographic ReviewIEEE Access10.1109/ACCESS.2018.28873417(4611-4628)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2887341
Toader BCantelmo GPopescu MViti F(2018)Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis2018 21st International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2018.8569935(993-998)Online publication date: Nov-2018
https://doi.org/10.1109/ITSC.2018.8569935
Lugano G(2018)Assessing Individual and Group Behavior from Mobility Data: Technological Advances and Emerging ApplicationsEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_219(93-101)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_219
Toader BSprumont FFaye SPopescu MViti F(2017)Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between IndividualsISPRS International Journal of Geo-Information10.3390/ijgi60300626:3(62)Online publication date: 24-Feb-2017
https://doi.org/10.3390/ijgi6030062
Lugano G(2017)Assessing Individual and Group Behavior from Mobility Data: Technological Advances and Emerging ApplicationsEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_219-1(1-9)Online publication date: 11-Sep-2017
https://doi.org/10.1007/978-1-4614-7163-9_219-1
Lin CHsu S(2016)Effective self-adjustment places of interest discovery in public placesInternational Journal of Ad Hoc and Ubiquitous Computing10.1504/IJAHUC.2016.07811422:4(226-235)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1504/IJAHUC.2016.078114
Albanna BMoawad IMoussa SSakr M(2015)Semantic Trajectories: A Survey from Modeling to ApplicationInformation Fusion and Geographic Information Systems (IF&GIS' 2015)10.1007/978-3-319-16667-4_4(59-76)Online publication date: 9-May-2015
https://doi.org/10.1007/978-3-319-16667-4_4
Sakib MHalim JHuang C(2014)Determining Location and Movement Pattern Using Anonymized WiFi Access Point BSSIDProceedings of the 2014 7th International Conference on Security Technology10.1109/SecTech.2014.10(11-14)Online publication date: 20-Dec-2014
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Liu HZhang YZhou YZhang DFu XRamakrishnan K(2014)Mining checkins from location-sharing services for client-independent IP geolocationIEEE INFOCOM 2014 - IEEE Conference on Computer Communications10.1109/INFOCOM.2014.6847987(619-627)Online publication date: Apr-2014
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Liao ZPan YPeng WLei PHe QIyengar ANejdl WPei JRastogi R(2013)On mining mobile apps usage behavior for predicting apps usage in smartphonesProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505529(609-618)Online publication date: 27-Oct-2013
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