A Generic Approach for Process Performance Analysis Using Bipartite Graph Matching
The field of process mining focuses on the analysis of event data, generated and captured during the execution of processes within companies. The majority of existing process mining techniques focuses on process discovery, i.e., automated (data-driven) discovery of a descriptive process model of the process, and conformance and/or compliance checking. However, to effectively improve processes, a detailed understanding in differences of the actual performance of a process, as well as the underlying causing factors, is needed. Surprisingly, few research focuses on generic techniques for process-aware data-driven performance measurement, analysis and prediction. Therefore, in this paper, we present a generic approach, which allows us to compute the average performance between arbitrary groups of activities active in a process. In particular, the technique requires no a priori knowledge of the process, and thus does not suffer from representational bias induced by any underlying process representation. Our experiments show that our approach is scalable to large cases and especially robust to recurrent activities in a case.