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Multidimensional modeling and analysis of wireless users online activity and mobility: a neural-networks map approach

Authors:

Saeed Moghaddam,

Ahmed HelmyAuthors Info & Claims

MSWiM '11: Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

Pages 401 - 408

https://doi.org/10.1145/2068897.2068965

Published: 31 October 2011 Publication History

Abstract

User online behavior and interests will play a central role in future mobile networks. We introduce a systematic method for large-scale multi-dimensional modeling and analysis of online activity and mobility for thousands of mobile users across 79 buildings over a variety of web domains. We propose a modeling approach based on kind of neural-networks, called self-organizing maps (SOM), for discovering, organizing and visualizing different mobile users' trends from billions of WLAN records. We find surprisingly that users' trends based on domains and locations can be accurately modeled using a self-organizing map with clearly distinct characteristics. We also find many non-trivial correlations between different types of web domains and locations.

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Cited By

G. Martín AFernández-Isabel AMartín de Diego IBeltrán M(2021)A survey for user behavior analysis based on machine learning techniques: current models and applicationsApplied Intelligence10.1007/s10489-020-02160-x51:8(6029-6055)Online publication date: 26-Jan-2021
https://dl.acm.org/doi/10.1007/s10489-020-02160-x
Moghaddam SHelmy A(2015)Interest-Based Mining and Modeling of Big Mobile NetworksProceedings of the 2015 IEEE First International Conference on Big Data Computing Service and Applications10.1109/BigDataService.2015.69(1-6)Online publication date: 30-Mar-2015
https://dl.acm.org/doi/10.1109/BigDataService.2015.69
Leng BLiu JPan HZhou STsinghua Z(2015)Topic model based behaviour modeling and clustering analysis for wireless network users2015 21st Asia-Pacific Conference on Communications (APCC)10.1109/APCC.2015.7412547(410-415)Online publication date: Oct-2015
https://doi.org/10.1109/APCC.2015.7412547
Show More Cited By

Index Terms

Multidimensional modeling and analysis of wireless users online activity and mobility: a neural-networks map approach
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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Information & Contributors

Information

Published In

cover image ACM Conferences

MSWiM '11: Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

October 2011

462 pages

ISBN:9781450308984

DOI:10.1145/2068897

General Chair:
Ahmed Helmy
University of Florida, USA
,
Program Chairs:
Bjorn Landfeldt
University of Sydney, Australia
,
Luciano Bononi
University of Bologna, Italy

Copyright © 2011 ACM.

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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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2011

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MSWiM '11

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SIGSIM

MSWiM '11: The 14th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

October 31 - November 4, 2011

Florida, Miami, USA

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Overall Acceptance Rate 398 of 1,577 submissions, 25%

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Citations

Cited By

G. Martín AFernández-Isabel AMartín de Diego IBeltrán M(2021)A survey for user behavior analysis based on machine learning techniques: current models and applicationsApplied Intelligence10.1007/s10489-020-02160-x51:8(6029-6055)Online publication date: 26-Jan-2021
https://dl.acm.org/doi/10.1007/s10489-020-02160-x
Moghaddam SHelmy A(2015)Interest-Based Mining and Modeling of Big Mobile NetworksProceedings of the 2015 IEEE First International Conference on Big Data Computing Service and Applications10.1109/BigDataService.2015.69(1-6)Online publication date: 30-Mar-2015
https://dl.acm.org/doi/10.1109/BigDataService.2015.69
Leng BLiu JPan HZhou STsinghua Z(2015)Topic model based behaviour modeling and clustering analysis for wireless network users2015 21st Asia-Pacific Conference on Communications (APCC)10.1109/APCC.2015.7412547(410-415)Online publication date: Oct-2015
https://doi.org/10.1109/APCC.2015.7412547
Xu CWang JZhao GTianfield H(2014)A geometric model for website evolution in mobile internetMultiagent and Grid Systems10.5555/2673997.267399910:2(95-108)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.5555/2673997.2673999
Moghaddam SHelmy AAguiar RGuo K(2014)Interest-aware implicit multicast (iCast)Proceedings of the 9th ACM workshop on Mobility in the evolving internet architecture10.1145/2645892.2645894(55-60)Online publication date: 11-Sep-2014
https://dl.acm.org/doi/10.1145/2645892.2645894
Boughareb DFarah N(2013)Identify the User's Information Need Using the Current Search ContextInternational Journal of Enterprise Information Systems10.4018/ijeis.20131001039:4(28-42)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.4018/ijeis.2013100103

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