Quantitative analysis of temporal patterns in loosely coupled active measurement results

Marat Zhanikeev, Yoshiaki Tanaka

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationManaging Next Generation Networks and Services - 10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007, Proceedings
Pages415-424
Number of pages10
Publication statusPublished - 2007 Dec 1
Event10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007 - Sapporo, Japan
Duration: 2007 Oct 102007 Oct 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4773 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007
CountryJapan
CitySapporo
Period07/10/1007/10/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • 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).