A Light-Weight Method of Concept Drift Detection using Heuristic Miner

Abstract

Processes of some business or life activities are sometimes changed due to some reasons, such as the emergence of new technologies and the change of the human behavior caused by a seasonal event, e.g. Christmas. Such changes are called concept drift. Detecting concept drift is useful for many reasons. For example, we can update existing out-of-date business rules. Many methods of concept drift detection in processes have been already proposed. However, most of them are a little bit complex because sliding widows should be defined on a log of business process during its analysis. We thus propose a light-weight method for its detection by using heuristic miner, which is a famous algorithm for process discovery. In our method, we simple observe the discovered model to identify the infrequent actions and transitions between actions. Our method helps us to identify several types of concept drift although some types cannot be detected. We discuss how to overcome current limitations of our method.