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Topographical proximity for mining network alarm data

Published: 22 August 2005 Publication History

Abstract

Increasingly powerful fault management systems are required to ensure robustness and quality of service in today's networks. In this context, event correlation is of prime importance to extract meaningful information from the wealth of alarm data generated by the network. Existing sequential data mining techniques address the task of identifying possible correlations in sequences of alarms. The output sequence sets, however, may contain sequences which are not plausible from the point of view of network topology constraints. This paper presents the Topographical Proximity (TP) approach which exploits topographical information embedded in alarm data in order to address this lack of plausibility in mined sequences. An evaluation of the quality of mined sequences is presented and discussed. Results show an improvement in overall system performance for imposing proximity constraints.

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    cover image ACM Conferences
    MineNet '05: Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
    August 2005
    296 pages
    ISBN:1595930264
    DOI:10.1145/1080173
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    Publication History

    Published: 22 August 2005

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    Author Tags

    1. event correlation
    2. fault data
    3. mining sequential patterns
    4. network configuration
    5. topographical proximity

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    SIGCOMM05: ACM SIGCOMM 2005 Conference
    August 26, 2005
    Pennsylvania, Philadelphia, USA

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    • (2018)Hierarchical Frequent Sequence Mining Algorithm for the Analysis of Alarm Cascades in Chemical ProcessesIEEE Access10.1109/ACCESS.2018.28684156(50197-50216)Online publication date: 2018
    • (2017)Data mining algorithm for correlation analysis of industrial alarmsCluster Computing10.1007/s10586-017-1170-3Online publication date: 19-Sep-2017
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