Clustering event logs using iterative partitioning

By A. Makanju, Nur Zincir-Heywood, Evangelos E. Milios. Knowledge Discovery and Data Mining, 2009

373 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/3b05a438d1b20d4170ef06485a40284480d6f84d

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. Zibin Zheng
5. Pinjia He
6. Jieming Zhu
7. Guineng Zheng
8. Vivek Srikumar

In Information Systems

1. Hongyu Zhang
2. Van-Hoang Le
3. Zibin Zheng
4. Michael R. Lyu
5. Aditya G. Parameswaran
6. Min Du
7. Jieming Zhu
8. Yihan Gao

Citations in the corpus

• 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].”
• Spell: Streaming Parsing of System Event Logs (2016, background): “There have been a substantial study on how to achieve this, for example, using regular expressions [8], leveraging the source code [6], or parsing purely based on system log characteristics using data mining approaches such as clustering and iterative partitioning [2], [9], [10], [11].”
• Navigating the Data Lake with DATAMARAN: Automatically Extracting Structure from Log Datasets (2017, methodology): “Other work clusters event logs [40, 56] by treating the lines of the log dataset as data points and assigning them to clusters.”
• this paper (2009, neutral): “We then demonstrate that AFR introduces negligible impacts on the metrics related to frame-rate (§6.2).”
• Experience Report: System Log Analysis for Anomaly Detection (2016, methodology): “He et al. [24] evaluate the effectiveness of four offline log parsing methods, SLCT [45], IPLOM [29], LogSig [44], and LKE [20] , which do not require system source code.”
• A Survey on Automated Log Analysis for Reliability Engineering (2020, neutral): “IPLoM [102, 103] contained three steps and partitioned log messages into groups in a hierarchical manner.”
• Tools and Benchmarks for Automated Log Parsing (2018, methodology): “3) Heuristics: By observing the characteristics of log messages, researchers proposed some log parsing methods (i.e., AEL [45], IPLoM [22], Spell [24], Drain [25]) based on heuristic rules.”
• DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning (2017, methodology): “First, a log parser [9, 14, 16, 23, 36, 37] is used to parse log entries to structured forms, which typically only contain “log keys” (or “message types”).”