Landmark detection from mobile life log using a modular Bayesian network model

Keum Sung Hwang, Sung-Bae Cho

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Mobile devices can now handle a great deal of information thanks to the convergence of diverse functionalities. Mobile environments have already shown great potential in terms of providing customized services to users because they can record meaningful and private information continually for long periods of time. Until now, most of this information has been generally ignored because of the limitations of mobile devices in terms of power, memory capacity and speed. In this paper, we propose a novel method that efficiently infers landmarks for users to overcome these problems. This method uses an effective probabilistic Bayesian network model for analyzing various kinds of log data in mobile environments, which were modularized in this paper to decrease complexity. We also present a cooperative inference method, and the proposed methods were evaluated with mobile log data generated and collected in the real world.

Original languageEnglish
Pages (from-to)12065-12076
Number of pages12
JournalExpert Systems with Applications
Volume36
Issue number10
DOIs
Publication statusPublished - 2009 Dec 1

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Bayesian networks
Mobile devices
Data storage equipment

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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Landmark detection from mobile life log using a modular Bayesian network model. / Hwang, Keum Sung; Cho, Sung-Bae.

In: Expert Systems with Applications, Vol. 36, No. 10, 01.12.2009, p. 12065-12076.

Research output: Contribution to journalArticle

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