PREDICTIVE MODELS FOR SUPPORT OF INCIDENT MANAGEMENT PROCESS IN IT SERVICE MANAGEMENT

• Main Authors: Martin SARNOVSKY, Juraj SURMA
• Format: Article
• Language: English
• Published: Technical University of Kosice 2018-03-01
• Series: Acta Electrotechnica et Informatica
• Subjects: IT service management; incident management; classification; data analysis
• Online Access: http://www.aei.tuke.sk/papers/2018/1/09_Sarnovsky.pdf

ABSTRACT The work presented in this paper is focused on creating of predictive models that help in the process of incident resolution and implementation of IT infrastructure changes to increase the overall support of IT management. Our main objective was to build the predictive models using machine learning algorithms and CRISP-DM methodology. We used the incident and related changes database obtained from the IT environment of the Rabobank Group company, which contained information about the processing of the incidents during the incident management process. We decided to investigate the dependencies between the incident observation on particular infrastructure component and the actual source of the incident as well as the dependency between the incidents and related changes in the infrastructure. We used Random Forests and Gradient Boosting Machine classifiers in the process of identification of incident source as well as in the prediction of possible impact of the observed incident. Both types of models were tested on testing set and evaluated using defined metrics.