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
Parents
179
Detecting large-scale system problems by mining console logs
Wei Xu, Ling Huang, Armando Fox et al. · 2009 · 1.3K citations
LogBERT: Log Anomaly Detection via BERT
Haixuan Guo, Shuhan Yuan, Xintao Wu · 2021 · 340 citations
Failure Prediction in IBM BlueGene/L Event Logs
Yinglung Liang, Yanyong Zhang, Hui Xiong et al. · 2007 · 276 citations
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252

Nankai University
Tianjin, China
6 h-index
11 publications
495 citations
Tsinghua University
Beijing, China
6 h-index
21 publications
1.4K citations
Huawei Technologies (China)
Shenzhen, China
6 h-index
19 publications
928 citations
University of Newcastle Australia
Newcastle, Australia
5 h-index
7 publications
1.2K citations
Baidu (China)
Beijing, China
5 h-index
16 publications
1K citations

Chinese Academy of Sciences
Beijing, China
5 h-index
8 publications
342 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
11 publications
841 citations

Beihang University
Beijing, China
4 h-index
17 publications
773 citations
1–8 of 252
Conference · 4 publications · 520 citations
4
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5 publications · 216 citations
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3 publications · 276 citations
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TNSM · 3 publications · 116 citations
3
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Repository · 2 publications · 32 citations
2
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1–5