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Paper Abstract and Keywords
Presentation 2019-01-17 09:40
Modeling of Utility Function for Real-time Prediction of Spatiotemporal Information
Keiichiro Sato, Ryoichi Shinkuma, Takehiro Sato, Eiji Oki (Kyoto Univ.), Takahiro Iwai, Takeo Onishi, Takahiro Nobukiyo, Dai Kanetomo, Kozo satoda (System platform Research Labs, NEC Corporation) MoNA2018-66
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, real-time prediction of spatiotemporal information has attracted a lot of attention. It is
expected that the amount of data traffic in mobile network will increase exponentially, which causes serious trans-
mission delays when traffic load is heavy. In real-time prediction of spatiotemporal information, if a part of data
used for prediction do not arrive within the deadline, the prediction accuracy degrades because the prediction is done
without using those missing data. The utility-based scheduling technique has been proposed as a way of prioritizing
such delay-sensitive data. However, no researchers have worked on utility-based scheduling for real-time prediction of
spatiotemporal information. Therefore, in this report, we propose a scheme that enables to model the utility function
real-time prediction of spatiotemporal information. Then, we demonstrate the model of the utility function obtained
using real spatiotemporal datasets.
Keyword (in Japanese) (See Japanese page) 
(in English) utility function / scheduling / spatiotemporal information / machine learning / feature selection / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 389, MoNA2018-66, pp. 51-55, Jan. 2019.
Paper # MoNA2018-66 
Date of Issue 2019-01-09 (MoNA) 
ISSN Online edition: ISSN 2432-6380
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All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee MoNA  
Conference Date 2019-01-16 - 2019-01-17 
Place (in Japanese) (See Japanese page) 
Place (in English) T. B. D. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MoNA 
Conference Code 2019-01-MoNA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Modeling of Utility Function for Real-time Prediction of Spatiotemporal Information 
Sub Title (in English)  
Keyword(1) utility function  
Keyword(2) scheduling  
Keyword(3) spatiotemporal information  
Keyword(4) machine learning  
Keyword(5) feature selection  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Keiichiro Sato  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Ryoichi Shinkuma  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Takehiro Sato  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Eiji Oki  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
5th Author's Name Takahiro Iwai  
5th Author's Affiliation System platform Research Labs, NEC Corporation (System platform Research Labs, NEC Corporation)
6th Author's Name Takeo Onishi  
6th Author's Affiliation System platform Research Labs, NEC Corporation (System platform Research Labs, NEC Corporation)
7th Author's Name Takahiro Nobukiyo  
7th Author's Affiliation System platform Research Labs, NEC Corporation (System platform Research Labs, NEC Corporation)
8th Author's Name Dai Kanetomo  
8th Author's Affiliation System platform Research Labs, NEC Corporation (System platform Research Labs, NEC Corporation)
9th Author's Name Kozo satoda  
9th Author's Affiliation System platform Research Labs, NEC Corporation (System platform Research Labs, NEC Corporation)
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Speaker Author-1 
Date Time 2019-01-17 09:40:00 
Presentation Time 25 minutes 
Registration for MoNA 
Paper # MoNA2018-66 
Volume (vol) vol.118 
Number (no) no.389 
Page pp.51-55 
#Pages
Date of Issue 2019-01-09 (MoNA) 


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