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 and 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) |
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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) |
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Keyword(1) |
Failure Prediction |
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Event Detection |
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Social Network |
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Machine Learning |
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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 |
Author-1 |
Date Time |
2017-09-19 15:20:00 |
Presentation Time |
30 minutes |
Registration for |
DE |
Paper # |
DE2017-20 |
Volume (vol) |
vol.117 |
Number (no) |
no.212 |
Page |
pp.123-128 |
#Pages |
6 |
Date of Issue |
2017-09-11 (DE) |
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