Open AccessA Survey on Risk Analysis in Information Technology Infrastructure Library (ITIL) Change Management Using Supervised Machine Learning
Author(s) -
Srushti Gajjar,
Mrugendrasinh Rahevar
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206371
Subject(s) - downtime , risk management , risk analysis (engineering) , information technology infrastructure library , computer science , capacity management , process management , change management (itsm) , risk management plan , it risk management , knowledge management , operations management , information technology , risk assessment , engineering , business , computer security , computer network , finance , lean manufacturing , operating system
Innovation in IT and technology leads to new developments within the organization. It is important for companies to respond more quickly to the changing trends in order to stay competitive. ITIL change management allows companies to introduce new technologies without interruption or downtime. It follows a standard practice to avoid any unwanted interruptions and involves evaluation, planning and approval of changes. Change Management is all about managing risk for the company and it is linked to the perception of risk that the company has. Risk Analysis is primary component when it comes to any software changes; organizations are concerned about risk management. For better performance by identifying and assessing risk in systematic manner is the aim of the risk management. In ITIL change management risk assessment is a manual process. Automation of risk analysis would have enormous benefits, like reducing the downtime, maximize the productivity and so on. So this paper is mainly on the survey of different supervised machine algorithms of machine learning, like support vector machine, Naive Bayes, logistic regressions.