Abstract
Event prediction is one of the most challenging problems in network monitoring systems. This type of inductive knowledge provides monitoring systems with valuable real time predictive capabilities. By obtaining this knowledge, system and network administrators can anticipate and prevent failures.
In this paper we present a prediction module for the monitoring software Osmius ( www.osmius.net ). Osmius has been developed by Peopleware ( peopleware.es ) under GPL licence. We have extended the Osmius database to store the knowledge we obtain from the algorithms in a highly parametrized way. Thus system administrators can apply the most appropriate settings for each system.
Results are presented in terms of positive predictive values and false discovery rates over a huge event database. They confirm that these pattern mining processes will provide network monitoring systems with accurate real time predictive capabilities.
This paper has been supported by Peopleware S.L. and the Project Osmius 2008, by the Spanish Ministry of Industry, Tourism and Commerce through the Plan Avanza R&D (TSI-020100-2008-58).
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García, R., Llana, L., Malagón, C., Pancorbo, J. (2010). Event Prediction in Network Monitoring Systems: Performing Sequential Pattern Mining in Osmius Monitoring Tool. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_49
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DOI: https://doi.org/10.1007/978-3-642-14400-4_49
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