TY  - GEN
A1  - Größl, Martin
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/23714/
N2  - In this thesis a failure prediction environment based on selected methods of statistics, machine learning and data mining was developed. For the system behavior representation of an observed information system mathematical models,such as Bayesian networks and Markov chains, were generated. These models were analyzed using a probabilistic model-checking for both the process evolution (path) into a fault condition as well as the time duration until the occurrence
of this possible fault.
Furthermore, based on a Kalman filter an approach for identification of anomalies and / or misbehavior in data streams was developed. This also includes a system identification part, which derives the models from measurement data.
These models form the basis for the misbehavior detection.
TI  - Konzeptioneller Ansatz einer Fehlerprädiktionsumgebung
Y1  - 2017///
ID  - heidok23714
AV  - public
ER  -