Data-driven validation, completion and construction of event relationship networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering models of behavior for concurrent workflows
Computers in Industry - Special issue: Process/workflow mining
Mining Temporal Patterns Without Predefined Time Windows
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Failure Diagnosis Using Decision Trees
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
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
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Discovering actionable patterns in event data
IBM Systems Journal
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
Proceedings of the 20th ACM international conference on Information and knowledge management
LogSig: generating system events from raw textual logs
Proceedings of the 20th ACM international conference on Information and knowledge management
Spatio-temporal decomposition, clustering and identification for alert detection in system logs
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Data summarization model for user action log files
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
An integrated framework for optimizing automatic monitoring systems in large IT infrastructures
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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In system management applications, an overwhelming amount of data are generated and collected in the form of temporal events. While mining temporal event data to discover interesting and frequent patterns has obtained rapidly increasing research efforts, users of the applications are overwhelmed by the mining results. The extracted patterns are generally of large volume and hard to interpret, they may be of no emphasis, intricate and meaningless to non-experts, even to domain experts. While traditional research efforts focus on finding interesting patterns, in this paper, we take a novel approach called event summarization towards the understanding of the seemingly chaotic temporal data. Event summarization aims at providing a concise interpretation of the seemingly chaotic data, so that domain experts may take actions upon the summarized models. Event summarization decomposes the temporal information into many independent subsets and finds well fitted models to describe each subset.