Drain: An Online Log Parsing Approach with Fixed Depth Tree

By Pinjia He, Jieming Zhu, Zibin Zheng, Michael R. Lyu. 2017 IEEE International Conference on Web Services (ICWS), 2017

961 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/6c0867762a8c64daa8a07133d1fe9f4ff59621c6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Hongyu Zhang
2. Van-Hoang Le
3. Michael R. Lyu
4. Haixuan Guo
5. Shuhan Yuan
6. Xintao Wu
7. Pinjia He
8. Shilin He

In Information Systems

1. Hongyu Zhang
2. Van-Hoang Le
3. Xintao Wu
4. Haixuan Guo
5. Shuhan Yuan
6. Michael R. Lyu
7. Pinjia He
8. Shilin He

Citations in the corpus

• Log-based Anomaly Detection Without Log Parsing (2021, methodology): “Table I shows examples of wrongly parsed log templates by the Drain parser [24].”
• A Survey on Automated Log Analysis for Reliability Engineering (2020, methodology): “P [59] All Yes Iterative partitioning MoLFI [108] All Yes Evolutionary algorithms Online SHISO [113] All No Clustering LenMa [131] All No Clustering Spell [41] All No Longest common subsequence Drain [60, 61] All Yes Heuristics Logram [33] All Yes Frequent pattern mining 5.2 Log Parser Characteristi”
• Robust log-based anomaly detection on unstable log data (2019, methodology): “Drain is proposed in [19] and is characterized by its high parsing accuracy and efficiency.”
• LogBERT: Log Anomaly Detection via BERT (2021, positive): “We adopt Drain [3] to parse the log messages into log keys.”