Towards Autonomic Computing: Effective Event Management
SEW '02 Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02)
Clustering intrusion detection alarms to support root cause analysis
ACM Transactions on Information and System Security (TISSEC)
An integrated framework on mining logs files for computing system management
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
What Supercomputers Say: A Study of Five System Logs
DSN '07 Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
Event summarization for system management
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing system logs: a new view of what's important
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
Alert Detection in System Logs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
One Graph Is Worth a Thousand Logs: Uncovering Hidden Structures in Massive System Event Logs
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Symptom-based problem determination using log data abstraction
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
System State Discovery Via Information Content Clustering of System Logs
ARES '11 Proceedings of the 2011 Sixth International Conference on Availability, Reliability and Security
System problem detection by mining console logs
System problem detection by mining console logs
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In this work, we propose an approach based on analyzing the spatio-temporal partitions of a system log, generated by supercomputers consisting of several nodes, for alert detection without employing semantic analysis. In this case, "Spatial" refers to the source of the log event and "Temporal" refers to the time the log event was reported. Our research shows that these spatio-temporal partitions can be clustered to separate normal activity from anomalous activity, with high accuracy. Therefore, our proposed method provides an effective alert detection mechanism.