Publications in Scientific Journals:

M. Wastian, M. Landsiedl, F. Breitenecker:
"A Soft Computing Model for Server Outage Detection";
Simulation Notes Europe, 25 (2015), 1; 27 - 34.

English abstract:
Abstract. Several approaches to detect or even predict
abnormal events as early as possible will be discussed.
The model input is a time series of frequently collected
data. The approaches presented in this document 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 focus will lie on the modular parts of the
proposed server outage model, starting with a discussion
about different approaches to time series prediction
such as SARIMA models and specific artificial neural
networks. After the presentation of several algorithms
for outlier detection (angle-based outlier factor, oneclass
support vector machines) the gained results of the
simulation are put up for discussion. The text ends with
an outlook for possible future work.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.