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
Mining logs files for data-driven system management
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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
An automated approach for abstracting execution logs to execution events
Journal of Software Maintenance and Evolution: Research and Practice - Special Issue on Program Comprehension through Dynamic Analysis (PCODA)
NetPal: a dynamic network administration knowledge base
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
Clustering event logs using iterative partitioning
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards insider threat detection using web server logs
Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies
Detecting large-scale system problems by mining console logs
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Visual and algorithmic tooling for system trace analysis: a case study
ACM SIGOPS Operating Systems Review
Automated analysis of load testing results
Proceedings of the 19th international symposium on Software testing and analysis
Mining console logs for large-scale system problem detection
SysML'08 Proceedings of the Third conference on Tackling computer systems problems with machine learning techniques
Chukwa: a system for reliable large-scale log collection
LISA'10 Proceedings of the 24th international conference on Large installation system administration
A graphical representation for identifier structure in logs
SLAML'10 Proceedings of the 2010 workshop on Managing systems via log analysis and machine learning techniques
Experience mining Google's production console logs
SLAML'10 Proceedings of the 2010 workshop on Managing systems via log analysis and machine learning techniques
Proceedings of the 2011 ACM Symposium on Applied Computing
Baler: deterministic, lossless log message clustering tool
Computer Science - Research and Development
Event log mining tool for large scale HPC systems
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Modeling and tolerating heterogeneous failures in large parallel systems
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
LogSig: generating system events from raw textual logs
Proceedings of the 20th ACM international conference on Information and knowledge management
A clustering model based on matrix approximation with applications to cluster system log files
ECML'05 Proceedings of the 16th European conference on Machine Learning
CAPRI: a tool for mining complex line patterns in large log data
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
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The complexity and cost of isolating the root cause of system problems in large parallel computers generally scales with the size of the system. Syslog messages provide a primary source of system feedback, but manual review is tedious and error prone. Informatic analysis can be used to detect subtle anomalies in the syslog message stream, thereby increasing the availability of the overall system. In This work the author describes the use of the bioinformatic-inspired Teiresias algorithm to automatically classify syslog messages, and compare it to an existing log analysis tool (SLCT). He then describes the use of occurrence statistics to group time-correlated messages, and present a simple graphical user interface for viewing analysis results. Finally, example analyses of syslogs from three independent clusters are presented.