Print Email Facebook Twitter Major incident detection Title Major incident detection Author Kolenbrander, Thomas (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Proksch, S. (mentor) van Deursen, A. (graduation committee) Tax, D.M.J. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science Date 2021-02-17 Abstract Incident management is one of the top priorities for IT companies. Within incident management the so-called major incidents, incidents with a severe impact on the company, require emergency actions to reduce this impact. An earlier detection of these major incidents will lead to a faster resolution time and this can be achieved by using software analytics methods. These methods have been used on incident management before which faces challenges with data quality and imbalance. However, software analytics have not been applied to major incidents in particular, which is what this thesis aims to do. To gain more insight into the possibilities and challenges of automated major incident detection, a case study at ING (a large global bank) was performed. For this case study a machine learning system has been created and assessed by eightexperts through interviews. Following from these interviews, three novel challenges were identified. The first challenge is that the impact should be measured to make a more accurate prediction. The second challenge is combining multiple information sources and the third challenge is the explainability of the decision. Furthermore, two solutions to existing challenges were investigated during the creation of the machine learning system. The first being the suitability of different machine learning models for incident data, as no direct comparison is available in literature. It is shown that Logistic Regression is best suited for this use case while the Support Vector Machine and Neural Network also perform well on incident data. Finally, some findings on thepre-processing of the incident data are reported. It is shown that assumptions in literature about automatically generated incident data being easier to use, can not always be made and that imbalanced data still remains an unsolved problem as sampling is not suited. The main contribution of this thesis are the insights and challenges in the unexplored topic of major incident detection and general recommendations for handling incident data. Subject incident managementsoftware analyticsmachine learningmajor incidentsit service management To reference this document use: http://resolver.tudelft.nl/uuid:8e0e459e-da61-4632-95ff-e0ba02c0c30c Embargo date 2021-04-01 Part of collection Student theses Document type master thesis Rights © 2021 Thomas Kolenbrander Files PDF MSc_Thesis_Thomas_Kolenbr ... _Final.pdf 1.68 MB Close viewer /islandora/object/uuid:8e0e459e-da61-4632-95ff-e0ba02c0c30c/datastream/OBJ/view