A data clustering algorithm for mining patterns from event logs

By Risto Vaarandi. IEEE International Workshop on IP Operations and Management, 2003

519 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4d7284cace291092721b3d2f12d78fe1c41b7747

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. Pinjia He
5. Shilin He
6. Jieming Zhu
7. Armando Fox
8. Wei Xu

In Information Systems

1. Hongyu Zhang
2. Michael R. Lyu
3. Pinjia He
4. Van-Hoang Le
5. Shilin He
6. Dongmei Zhang
7. Junjie Chen
8. Armando Fox

In Signal Processing

1. Michael R. Lyu
2. Van-Hoang Le
3. Armando Fox
4. Wei Xu
5. Michael I. Jordan
6. David A. Patterson
7. Shen Furao
8. Jieming Zhu

Citations in the corpus

• Detecting large-scale system problems by mining console logs (2009, methodology): “SLCT [32], Loghound [33], Sisyphus [27], and [7] use more advanced clustering and association rules, as well as scoring methods from information retrieval to ex­tract message templates for log parsing.”
• Robust log-based anomaly detection on unstable log data (2019, methodology): “He, et al. [17] evaluated four commonly used log parsers, including SLCT [40], IPLoM [32], LKE [12] and LogSig [39].”
• A Survey on Automated Log Analysis for Reliability Engineering (2020, methodology): “ng from 2003, a line of research efforts have been contributed to automated rule construction and critical information extraction from software logs, including the first pieces of work of log parsing [138], anomaly detection [138], and failure prediction [125]. In addition, in the same year, Hätönen”
• Experience Report: System Log Analysis for Anomaly Detection (2016, methodology): “There are two types of log parsing methods: clustering-based (e.g., LKE [20], LogSig [44] ) and heuristic-based (e.g., iPLoM [29], SLCT [45]).”
• Log-based Anomaly Detection Without Log Parsing (2021, methodology): “He et al [16] evaluated four widely used log parsers, including SLCT [57], IPLoM [26], LKE [58] and LogSig [20].”
• this paper (2009, unknown): “We present the performance improvements: The ratio of frames with long queuing delay and total delay of AFR has been improved by 2.1 × -26 × and 13%-2.2 × against existing baselines (§6.1).”