Alexander Seeliger
Timo Nolle
Max Mühlhäuser
ABSTRACT
Work in organisations is often structured into business processes, implemented using process-aware information systems (PAISs). These systems aim to enforce employees to perform work in a certain way, executing tasks in a specified order. However, the execution strategy may change over time, leading to expected and unexpected changes in the overall process. Especially the unexpected changes may manifest without notice, which can have a big impact on the performance, costs, and compliance. Thus it is important to detect these hidden changes early in order to prevent monetary consequences. Traditional process mining techniques are unable to identify these execution changes because they usually generalise without considering time as an extra dimension, and assume stable processes. Most algorithms only produce a single process model, reflecting the behaviour of the complete analysis scope. Small changes cannot be identified as they only occur in a small part of the event log. This paper proposes a method to detect process drifts by performing statistical tests on graph metrics calculated from discovered process models. Using process models allows to additionally gather details about the structure of the drift to answer the question which changes were made to the process.
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