  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.
