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Journal of Central South University of Technology

, Volume 16, Issue 6, pp 976–981 | Cite as

Log integration on large scale for global networking monitoring

  • Jia-jia Miao (缪嘉嘉)Email author
  • Quan-yuan Wu (吴泉源)
  • Yan Jia (贾 焰)
Article
  • 28 Downloads

Abstract

Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning (IML) approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering (SBC) approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holistically with 85% recall rate when there are 38 data sources.

Key words

machine-learning clustering data integration schema matching instance matching 

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Copyright information

© Central South University Press and Springer Berlin Heidelberg 2009

Authors and Affiliations

  • Jia-jia Miao (缪嘉嘉)
    • 1
    • 2
    Email author
  • Quan-yuan Wu (吴泉源)
    • 1
  • Yan Jia (贾 焰)
    • 1
  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.Institute of Command AutomationPLA University of Science and TechnologyNanjingChina

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