Abstract:
Computer systems must respond to frequently changing user needs. These increasingly large and complex systems are
difficult to maintain. A change in a business process is a difficult task especially if the process is running, where a small change can
affect the rest of the system with considerable and undesirable impacts.
In this work, we focus on the problem of studying the impact of change in Business Process Management (BPM). We propose a
predictive approach to help business process designers make a decision before implementing changes by using machine learning
algorithms to predict the degree or level of change in a business process. The experiments conducted in this study show the
performance of SVM and Guassian Naïve Bayes algorithms
