Machine Learning For Log Analysis

Machine Learning for Log Analysis is a computational methodology that applies machine learning algorithms to automatically extract, classify, and infer patterns from system log data for the purpose of anomaly detection, fault diagnosis, or operational insight generation.

179

Publications

9.7K

Citations

667

Authors

252

Institutions

Publications per year

2017–2026

137

Authors

667

  1. WM
    Weibin Meng

    Tsinghua University, China

    7 publications · 416 citations

    7

    h-index

  2. HZ
    Hongyu Zhang

    University of Newcastle Australia, Australia

    5 publications · 669 citations

    5

    h-index

  3. SZ
    Shenglin Zhang

    Tsinghua University, China

    5 publications · 312 citations

    5

    h-index

  4. DP
    Dan Pei

    Tsinghua University, China

    5 publications · 312 citations

    5

    h-index

  5. LS
    Lei Song

    Baidu (China), China

    5 publications · 312 citations

    5

    h-index

1–5 of 667

Venues

  1. 4

    h-index

  2. Electronics

    5 publications · 216 citations

    4

    h-index

  3. Journal of Petroleum Science and Engineering

    3 publications · 276 citations

    3

    h-index

  4. IEEE Transactions on Network and Service Management

    TNSM · 3 publications · 116 citations

    3

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 32 citations

    2

    h-index

1–5