Constructing decision trees from process logs for performer recommendation

Abstract

This paper demonstrates that the discovery technique using historical event logs can be extended to predict business performance and recommend performers for running instances. For the prediction and recommendation, we adopt decision trees, which is a decision support tool in management science. Decision trees are commonly used to help identify the most likely alternative to reach a goal. To provide effective performer recommendation, we use several filters with previous performers and key tasks to the decision tree. These filters allow for a suitable recommendation according to the characteristics of the processes. The proposed approach is implemented on ProM framework and it is then evaluated through an experiment using reallife event logs, taken from a Dutch Financial Institute. The main contribution of this paper is to provide a real-time decision support tool by recommendation of the best performer for a target performance indicator during process execution based on historical data. © Springer International Publishing Switzerland 2014.