Discord Monitoring for Streaming Time-Series

Kato, Shinya; Amagata, Daichi; Nishio, Shunya; Hara, Takahiro

doi:https://doi.org/10.1007/978-3-030-27615-7_6

Discord Monitoring for Streaming Time-Series

Kato, Shinya; Amagata, Daichi; Nishio, Shunya et al.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11706, 79-94

Number of Access:618（2025-08-16 09:38 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93074

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Title	Discord Monitoring for Streaming Time-Series
Creator	CreatorName Kato, Shinya
	NRID 1000040770649 1000040770649 researchmap permalink amgt amgt Scopus Author ID 55811167300 55811167300 CreatorName Amagata, Daichi
	CreatorName Nishio, Shunya
	NRID 1000020294043 1000020294043 ORCID 0000-0003-4807-3156 0000-0003-4807-3156 researchmap permalink hara-osakau hara-osakau Scopus Author ID 7404213547 7404213547 CreatorName Hara, Takahiro
Subject	Discord
	Sliding window
	Time-series
Description	This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-27615-7_6. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.
Description	Kato S., Amagata D., Nishio S., et al. Discord Monitoring for Streaming Time-Series. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11706 LNCS, 79 (2019)
Abstract	Many applications generate time-series and analyze it. One of the most important time-series analysis tools is anomaly detection, and discord discovery aims at finding an anomaly subsequence in a time-series. Time-series is essentially dynamic, so monitoring the discord of a streaming time-series is an important problem. This paper addresses this problem and proposes SDM (Streaming Discord Monitoring), an algorithm that efficiently updates the discord of a streaming time-series over a sliding window. We show that SDM is approximation-friendly, i.e., the computational efficiency is accelerated by monitoring an approximate discord with theoretical bound. Our experiments on real datasets demonstrate the efficiency of SDM and its approximate version.
Conference Information	Conference Name International Conference on Database and Expert Systems Applications - DEXA 2019 Conference Sequence 30 Conference Date August 26th to August 29th, 2019 Conference Venue Johannes Kepler University Linz Conference Place Linz Conference Country
Publisher	Springer
Source Title	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume (Issue)	11706
Page	79-94
Date of Issued	2019-08-03
Language	English
Handle URL	https://hdl.handle.net/11094/93074
PISSN	03029743
eISSN	16113349
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1007/978-3-030-27615-7_6
Relation.isPartOf	ISBN 9783030276140
Relation.isPartOf	ISBN 9783030276157
Access Rights	open access
CopyRight	© 2019, Springer Nature Switzerland AG.
oaire:version	Accepted Manuscript
Category	会議発表論文 Conference Paper

Resource Type	conference paper
Local Type	Conference Paper
DCMI Type	Text
DCTERMS.bibliographicCitation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2019, 11706, p. 79-94
DC.title	Discord Monitoring for Streaming Time-Series
DC.creator	Kato, Shinya
	Amagata, Daichi
	Nishio, Shunya
	Hara, Takahiro
DC.publisher	Springer
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2019-08-03
DC.identifier	https://doi.org/10.1007/978-3-030-27615-7_6
DC.subject	Discord
	Sliding window
	Time-series
DC.description	This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-27615-7_6. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.
DC.description	Kato S., Amagata D., Nishio S., et al. Discord Monitoring for Streaming Time-Series. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11706 LNCS, 79 (2019)
DCTERMS.abstract	Many applications generate time-series and analyze it. One of the most important time-series analysis tools is anomaly detection, and discord discovery aims at finding an anomaly subsequence in a time-series. Time-series is essentially dynamic, so monitoring the discord of a streaming time-series is an important problem. This paper addresses this problem and proposes SDM (Streaming Discord Monitoring), an algorithm that efficiently updates the discord of a streaming time-series over a sliding window. We show that SDM is approximation-friendly, i.e., the computational efficiency is accelerated by monitoring an approximate discord with theoretical bound. Our experiments on real datasets demonstrate the efficiency of SDM and its approximate version.
DC.rights	© 2019, Springer Nature Switzerland AG.
DC.format	93074
citation_title	Discord Monitoring for Streaming Time-Series
citation_author	Kato, Shinya
	Amagata, Daichi
	Nishio, Shunya
	Hara, Takahiro
citation_publisher	Springer
citation_language	English
citation_date	2019-08-03
citation_journal_title	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
citation_volume	11706
citation_firstpage	79
citation_lastpage	94
citation_doi	https://doi.org/10.1007/978-3-030-27615-7_6
citation_public_url	https://hdl.handle.net/11094/93074

Discord Monitoring for Streaming Time-Series

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