• Guillaume Bouchard , Jean-Marc Andreoli
The Sixth International conference on Machine Learning and Applications (ICMLA 07), Cincinnati, Ohio, USA, 13-15 Dec.2007.
In modern business, educational, and otehr settings, it is common to provide a digital network that interconnects hardware devices for shared access by users (e.g., in an office where printers are available for use by all the office workers). In such a context, so-called soft failures, where a device silently starts working in a degraded mode, may easily go un-notices for a long time, resulting in potential productivity loss. It is therefore advantageous to enable system administrators to identify soft failures at an early stage. We propose here a probabilistic method using variational inference on a factorial hidden Markov model to automatically discover soft failures, based on the analysis of simple usage information which is nomally logged by the network infrastructure. We propose to mine these logs in order to discover statistically significant deviations in the usage behaviour of the overall infrastructure, and we identify such deviations with soft failures, or, in any case, situations of interest to an administrator.
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