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Paper Abstract and Keywords
Presentation 2017-09-19 15:20
Model Ensemble for Failure Event Detection using Multiple User Activity Data on the Web
Motoyuki Oki (NTT Communications), Koh Takeuchi (NTT), Yukio Uematsu (NTT Communications) DE2017-20
Abstract (in Japanese) (See Japanese page) 
(in English) Mobile network service providers aim to maintain stable operation and improve service performance using multiple user's activity data. The data is sometimes corrupted by a number of unusual behavior such as service failure occurrence. In this paper, we address the detection and forecasting problems of finding and extracting failure event. We collect tweets related to the service, search queries on a search box, and web access logs from official web pages. We present the feature extraction for text classification problems and informative representation of the time series. We propose ensemble event detection and forecasting methods using supervised classification models and unsupervised anomaly detection models. Experiments to detect and predict multiple failure events in a content service provider demonstrate the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Failure Prediction / Event Detection / Social Network / Machine Learning / Ensemble Model / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 212, DE2017-20, pp. 123-128, Sept. 2017.
Paper # DE2017-20 
Date of Issue 2017-09-11 (DE) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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reproduction
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)
Download PDF DE2017-20

Conference Information
Committee DE IPSJ-DBS IPSJ-IFAT  
Conference Date 2017-09-18 - 2017-09-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Ochanomizu University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Big Data Management, Information Retrieval, Knowledge Discovery, etc. 
Paper Information
Registration To DE 
Conference Code 2017-09-DE-DBS-IFAT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Model Ensemble for Failure Event Detection using Multiple User Activity Data on the Web 
Sub Title (in English)  
Keyword(1) Failure Prediction  
Keyword(2) Event Detection  
Keyword(3) Social Network  
Keyword(4) Machine Learning  
Keyword(5) Ensemble Model  
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1st Author's Name Motoyuki Oki  
1st Author's Affiliation NTT Communications Corporation (NTT Communications)
2nd Author's Name Koh Takeuchi  
2nd Author's Affiliation NTT Communication Science Laboratories (NTT)
3rd Author's Name Yukio Uematsu  
3rd Author's Affiliation NTT Communications Corporation (NTT Communications)
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Speaker
Date Time 2017-09-19 15:20:00 
Presentation Time 30 
Registration for DE 
Paper # IEICE-DE2017-20 
Volume (vol) IEICE-117 
Number (no) no.212 
Page pp.123-128 
#Pages IEICE-6 
Date of Issue IEICE-DE-2017-09-11 


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