2013
EMSS 2013 Proceeding
Using data mining and machine learning methods for server outage detection ? modelling normality and anomalies
Authors: Matthias Wastian, Felix Breitenecker, Michael Landsiedl
Abstract
This paper will discuss several approaches to detect abnormal events, which are considered to be worth further investigation by the modeler, in a time series of frequently collected data as early as possible and ? wherever applicable ? to predict them. The approaches to this task use various methods originating in the field of data mining, machine learning and soft computing in a hybrid manner. After a basic introduction including several areas of application, the paper will focus on the modular parts of the proposed methodology, starting with a discussion about different approaches to predict time series. After the presentation of several algorithms for outlier detection, which are applicable not only for time series, but also a chain of events, the results of the simulation gained in a project to detect server outages as early as possible are put up for discussion. The text ends with an outlook for possible future work.
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