Failure diagnosis using decision trees

By Mike Y. Chen, Alice X. Zheng, J. Lloyd, Michael I. Jordan, E. Brewer. International Conference on Autonomic Computing, 2004. Proceedings., 2004

495 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0d384a572efe5d82a863155798b328148cc5fc50

How researchers see this paper

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

1. Van-Hoang Le
2. Shilin He
3. Hongyu Zhang
4. Pinjia He
5. Jieming Zhu
6. Haixuan Guo
7. Qingwei Lin
8. Xu Zhang

Citations in the corpus

• Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis (2009, background): “Therefore, developing automatic execution anomaly monitoring and detection tools becomes an essential requirement of many distributed systems to ensure the Quality of Service.”
• Robust log-based anomaly detection on unstable log data (2019, background): “There have been a lot of studies on log-based anomaly detection[2, 4, 7, 11, 12, 14, 15, 20, 26, 28, 29, 33–35, 38, 41].”
• Microreboot - A Technique for Cheap Recovery (2004, background): “Clusters of 2-4 J2EE servers are typical in enterprise settings, with high-end financial and telecom applications running on 10-24 nodes [15]; a few gigantic services, like eBay’s online auction service, run on pools of clusters totaling 2000 application servers [11].”
• A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “[91, 92] use DTs and clustering to diagnose faults in large network systems.”
• Experience Report: System Log Analysis for Anomaly Detection (2016, methodology): “Similar to Logistic Regression and Decision Tree, the training instances are event count vectors together with their labels.”
• MacroBase: Prioritizing Attention in Fast Data (2016, methodology): “The literature contains several recent explanation techniques leveraging decision-tree [23] and Apriori-like [39, 80] pruning, grid search [68, 82], data cubing [69], Bayesian statistics [78], visualization [17,62], causal reasoning [81],”
• Log-based Anomaly Detection Without Log Parsing (2021, methodology): “For example, SVM [30] and LR [6], which transform log sequences into log count vectors, cannot take new log events as input because the dimension of log count vectors is fixed (i.”
• MacroBase: Analytic Monitoring for the Internet of Things (2016, methodology): “OpenGamma [2]. Data explanation. Data explanation techniques assist in summarizing differences between datasets. The literature contains several recent explanation techniques leveraging decision-tree [23] and Apriori-like [39,80] pruning, grid search [68,82], data cubing [69], Bayesian statistics [7”
• LogBERT: Log Anomaly Detection via BERT (2021, methodology): “[42] utilize decision trees for failure diagnoses.”
• Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure (2018, methodology): “Other works have used decision trees (Chen et al. 2004), support vector machines (Widodo and Yang 2007),”