Predicting user activities in the sequence of mobile context for ambient intelligence environment using dynamic Bayesian network

Han Saem Park, Sung-Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Recently, mobile devices became essential mediums in order to implement ambient intelligence. Since people can always keep these mobile devices, it is easy for them to collect diverse user information. Therefore, many research groups have attempted to provide useful services based on this ubiquitous information. This paper proposes a method to predict user activity in the sequence of mobile context. In order to conduct accurate prediction of activity among various patterns, we have considered user activity, place, time and day of week as mobile context. We have used dynamic Bayesian network to model the user activity patterns with this context, and learned the model of each individual to obtain better model. For experiments, we have collected the mobile logs of undergraduate students, and confirmed that the proposed method produced good performance.

Original languageEnglish
Title of host publicationICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
Pages311-316
Number of pages6
Publication statusPublished - 2010 Sep 9
Event2nd International Conference on Agents and Artificial Intelligence, ICAART 2010 - Valencia, Spain
Duration: 2010 Jan 222010 Jan 24

Publication series

NameICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
Volume1

Other

Other2nd International Conference on Agents and Artificial Intelligence, ICAART 2010
CountrySpain
CityValencia
Period10/1/2210/1/24

Fingerprint

Bayesian networks
Mobile devices
Students
Ambient intelligence
Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Park, H. S., & Cho, S-B. (2010). Predicting user activities in the sequence of mobile context for ambient intelligence environment using dynamic Bayesian network. In ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings (pp. 311-316). (ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings; Vol. 1).
Park, Han Saem ; Cho, Sung-Bae. / Predicting user activities in the sequence of mobile context for ambient intelligence environment using dynamic Bayesian network. ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings. 2010. pp. 311-316 (ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings).
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Park, HS & Cho, S-B 2010, Predicting user activities in the sequence of mobile context for ambient intelligence environment using dynamic Bayesian network. in ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings. ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings, vol. 1, pp. 311-316, 2nd International Conference on Agents and Artificial Intelligence, ICAART 2010, Valencia, Spain, 10/1/22.

Predicting user activities in the sequence of mobile context for ambient intelligence environment using dynamic Bayesian network. / Park, Han Saem; Cho, Sung-Bae.

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings. 2010. p. 311-316 (ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings; Vol. 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Recently, mobile devices became essential mediums in order to implement ambient intelligence. Since people can always keep these mobile devices, it is easy for them to collect diverse user information. Therefore, many research groups have attempted to provide useful services based on this ubiquitous information. This paper proposes a method to predict user activity in the sequence of mobile context. In order to conduct accurate prediction of activity among various patterns, we have considered user activity, place, time and day of week as mobile context. We have used dynamic Bayesian network to model the user activity patterns with this context, and learned the model of each individual to obtain better model. For experiments, we have collected the mobile logs of undergraduate students, and confirmed that the proposed method produced good performance.

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Park HS, Cho S-B. Predicting user activities in the sequence of mobile context for ambient intelligence environment using dynamic Bayesian network. In ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings. 2010. p. 311-316. (ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings).