[Zurück]

M. Wastian, F. Breitenecker, M. Landsiedl:
"Using data mining and machine learning methods for server outage detection - modelling normality and anomalies";
in: "The 25th European Modeling & Simulation Symposium", A. Bruzzone, E. Jimenez, F. Longo, Y. Merkuryev (Hrg.); herausgegeben von: University di Genova; The 25th European Modeling & Simulation Symposium, Rende (CS), 2013, ISBN: 978-88-97999-22-5, S. 647 - 653.

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.

abnormal event detection, prediction, data mining, machine learning

http://publik.tuwien.ac.at/files/PubDat_224745.pdf