Detecting Concept Drift in Processes using Graph Metrics on Process Graphs

Alexander Seeliger, Timo Nolle, Max Mühlhäuser
2017 Proceedings of the 9th Conference on Subject-oriented Business Process Management - S-BPM ONE '17  
Work in organisations is o en structured into business processes, implemented using process-aware information systems (PAISs). ese systems aim to enforce employees to perform work in a certain way, executing tasks in a speci ed 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. us it is
more » ... mportant 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, re ecting the behaviour of the complete analysis scope. Small changes cannot be identi ed as they only occur in a small part of the event log. is paper proposes a method to detect process dri s 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 dri to answer the question which changes were made to the process.
doi:10.1145/3040565.3040566 fatcat:swcfxszugzcb3chy5u2wz7b2py