During the lifetime of a Business Process changes can be made to the workflow, the required resources, required documents, . . . . Different traces from the same Business Process within a single log file can thus differ substantially due to these changes. We propose a method that is able to detect concept drift in multivariate log files with a dozen attributes. We test our approach on the BPI Challenge 2018 data con- sisting of applications for EU direct payment from farmers in Germany where we use it to detect Concept Drift. In contrast to other methods our algorithm does not require the manual selection of the features used to detect drift. Our method first creates a model that captures the re- lations between attributes and between events of different time steps. This model is then used to score every event and trace. These scores can be used to detect outlying cases and concept drift. Thanks to the decomposability of the score we are able to perform detailed root-cause analysis.
@cite_3: Operational processes need to change to adapt to changing circumstances, e.g., new legislation, extreme variations in supply and demand, seasonal effects, etc.While the topic of flexibility is well-researched in the BPM domain, contemporary process mining approaches assume the process to be in steady state. When discovering a process model from event logs, it is assumed that the process at the beginning of the recorded period is the same as the process at the end of the recorded period. Obviously, this is often not the case due to the phenomenon known as concept drift. While cases are being handled, the process itself may be changing. This paper presents an approach to analyze such second-order dynamics. The approach has been implemented in ProM1 and evaluated by analyzing an evolving process.
In @cite_3 a concept drift detection method is proposed that uses statistical tests over feature vectors. These feature vectors are based on the order in which activities occur in the workflow. They record for example how many activities always, sometimes, or never occur after a given activity. Other measures can be used to add extra features that may incorporate the other attributes in the log. The method does require manual selection of the exact features to be used in the statistical tests.