Presentation 2010-07-23
A location prediction method using dynamic Bayesian networks with ambiguous calendar data and GPS data
Masaaki NISHINO, Yukihiro NAKAMURA, Takashi YAGI, Shinyo MUTO, Masanobu ABE,
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Abstract(in English) We propose a method for making prediction of future positions of a user by using two different types of logged data - GPS data and calendar data. We tied event descriptions on calendar data to locations by utilizing co-occurrences relations with GPS data, and make a Dynamic Bayesian Network model for prediction. With this combination, we expect that we can predict unusual movements that cannot be predicted with only GPS data, and improve results with ambiguous Calendar data. We evaluated features of our models with experiments using real GPS and calendar data.
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Keyword(in English) Global Positioning System / Calendar / Dynamic Bayesian Networks / Place Prediction
Paper # LOIS2010-15
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Conference Date 2010/7/15(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A location prediction method using dynamic Bayesian networks with ambiguous calendar data and GPS data
Sub Title (in English)
Keyword(1) Global Positioning System
Keyword(2) Calendar
Keyword(3) Dynamic Bayesian Networks
Keyword(4) Place Prediction
1st Author's Name Masaaki NISHINO
1st Author's Affiliation NTT Cyber Solutions Laboratories, NTT Corporation()
2nd Author's Name Yukihiro NAKAMURA
2nd Author's Affiliation NTT Cyber Solutions Laboratories, NTT Corporation
3rd Author's Name Takashi YAGI
3rd Author's Affiliation NTT Cyber Solutions Laboratories, NTT Corporation
4th Author's Name Shinyo MUTO
4th Author's Affiliation NTT Cyber Solutions Laboratories, NTT Corporation
5th Author's Name Masanobu ABE
5th Author's Affiliation NTT Cyber Solutions Laboratories, NTT Corporation
Date 2010-07-23
Paper # LOIS2010-15
Volume (vol) vol.110
Number (no) 141
Page pp.pp.-
#Pages 6
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