Quantitative analysis of temporal patterns in loosely coupled active measurement results

Marat Zhanikeev; Yoshiaki Tanaka

Quantitative analysis of temporal patterns in loosely coupled active measurement results

Marat Zhanikeev^*, Yoshiaki Tanaka

^*Corresponding author for this work

School of Fundamental Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

With constantly increasing complexity of active measurement methods, the issue of processing measurement results becomes important. Similarly to traditional pattern discovery, temporal patterns found in active measurement samples should be provided effective storage and means to compare to other samples. Traditional time series data mining is not applicable to temporal patterns in active measurement time series. This paper proposes a pattern discovery method based on unique features of active measurement results. The method is implemented in form of a database and is used in the paper to verify the proposed method.

Original language	English
Title of host publication	Managing Next Generation Networks and Services - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Proceedings
Pages	415-424
Number of pages	10
Publication status	Published - 2007 Dec 1
Event	10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007 - Sapporo, Japan Duration: 2007 Oct 10 → 2007 Oct 12

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4773 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007
Country/Territory	Japan
City	Sapporo
Period	07/10/10 → 07/10/12

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Cite this

Zhanikeev, M., & Tanaka, Y. (2007). Quantitative analysis of temporal patterns in loosely coupled active measurement results. In Managing Next Generation Networks and Services - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Proceedings (pp. 415-424). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4773 LNCS).

Quantitative analysis of temporal patterns in loosely coupled active measurement results. / Zhanikeev, Marat; Tanaka, Yoshiaki.
Managing Next Generation Networks and Services - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Proceedings. 2007. p. 415-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4773 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhanikeev, M & Tanaka, Y 2007, Quantitative analysis of temporal patterns in loosely coupled active measurement results. in Managing Next Generation Networks and Services - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4773 LNCS, pp. 415-424, 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Sapporo, Japan, 07/10/10.

Zhanikeev M, Tanaka Y. Quantitative analysis of temporal patterns in loosely coupled active measurement results. In Managing Next Generation Networks and Services - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Proceedings. 2007. p. 415-424. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Zhanikeev, Marat ; Tanaka, Yoshiaki. / Quantitative analysis of temporal patterns in loosely coupled active measurement results. Managing Next Generation Networks and Services - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Proceedings. 2007. pp. 415-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{791a7cf8a82d4909a679f8663df8e640,

title = "Quantitative analysis of temporal patterns in loosely coupled active measurement results",

abstract = "With constantly increasing complexity of active measurement methods, the issue of processing measurement results becomes important. Similarly to traditional pattern discovery, temporal patterns found in active measurement samples should be provided effective storage and means to compare to other samples. Traditional time series data mining is not applicable to temporal patterns in active measurement time series. This paper proposes a pattern discovery method based on unique features of active measurement results. The method is implemented in form of a database and is used in the paper to verify the proposed method.",

author = "Marat Zhanikeev and Yoshiaki Tanaka",

year = "2007",

month = dec,

day = "1",

language = "English",

isbn = "9783540754756",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "415--424",

booktitle = "Managing Next Generation Networks and Services - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Proceedings",

note = "10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007 ; Conference date: 10-10-2007 Through 12-10-2007",

}

TY - GEN

T1 - Quantitative analysis of temporal patterns in loosely coupled active measurement results

AU - Zhanikeev, Marat

AU - Tanaka, Yoshiaki

PY - 2007/12/1

Y1 - 2007/12/1

N2 - With constantly increasing complexity of active measurement methods, the issue of processing measurement results becomes important. Similarly to traditional pattern discovery, temporal patterns found in active measurement samples should be provided effective storage and means to compare to other samples. Traditional time series data mining is not applicable to temporal patterns in active measurement time series. This paper proposes a pattern discovery method based on unique features of active measurement results. The method is implemented in form of a database and is used in the paper to verify the proposed method.

AB - With constantly increasing complexity of active measurement methods, the issue of processing measurement results becomes important. Similarly to traditional pattern discovery, temporal patterns found in active measurement samples should be provided effective storage and means to compare to other samples. Traditional time series data mining is not applicable to temporal patterns in active measurement time series. This paper proposes a pattern discovery method based on unique features of active measurement results. The method is implemented in form of a database and is used in the paper to verify the proposed method.

UR - http://www.scopus.com/inward/record.url?scp=38149064512&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38149064512&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:38149064512

SN - 9783540754756

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 415

EP - 424

BT - Managing Next Generation Networks and Services - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Proceedings

T2 - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007

Y2 - 10 October 2007 through 12 October 2007

ER -

Quantitative analysis of temporal patterns in loosely coupled active measurement results

Abstract

Publication series

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this