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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages415-424
    Number of pages10
    Volume4773 LNCS
    Publication statusPublished - 2007
    Event10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007 - Sapporo
    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)03029743
    ISSN (Electronic)16113349

    Other

    Other10th Asia-Pacific Network Operations and Management Symposium, APNOMS 2007
    CitySapporo
    Period07/10/1007/10/12

    Fingerprint

    Quantitative Analysis
    Pattern Discovery
    Chemical analysis
    Time series
    Data Mining
    Data mining
    Time Series Data
    Databases
    Verify
    Processing

    ASJC Scopus subject areas

    • Computer Science(all)
    • Biochemistry, Genetics and Molecular Biology(all)
    • Theoretical Computer Science

    Cite this

    Zhanikeev, M., & Tanaka, Y. (2007). Quantitative analysis of temporal patterns in loosely coupled active measurement results. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4773 LNCS, 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.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4773 LNCS 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 proceedingConference contribution

    Zhanikeev, M & Tanaka, Y 2007, Quantitative analysis of temporal patterns in loosely coupled active measurement results. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4773 LNCS, 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, 07/10/10.
    Zhanikeev M, Tanaka Y. Quantitative analysis of temporal patterns in loosely coupled active measurement results. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4773 LNCS. 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4773 LNCS 2007. pp. 415-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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