Skip Probabilities for Subprocesses

International Conference on Process Mining (ICPM), pp. 1–8

Abstract

Conformance checking techniques compare process models of organizational behavior with observed process executions to reveal their deviations. Traditional alignments concern individual activities and provide a single out of potentially infinitely many explanations for observed deviations. Skip alignments lift insights to subprocesses and provide all possible explanations. Though valuable for analysts and process mining tools, there exist no interpretations how likely these deviations are. In this paper, we introduce skip probabilities revealing how likely certain subprocesses deviate w.r.t. an event log of observed process executions. We show the formal derivation of this calculation and demonstrate the feasibility of its computation. By analyzing a realistic case, we empirically show that yet hidden process insights can be derived from skip probabilities and how they contribute to targeted process improvement.

Authors 4

1. Philipp Bär Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,Germany

2. Adam T. Burke

   Queensland University of Technology

   Affiliation as printed

   Queensland University of Technology,Brisbane,Australia

3. Moe Thandar Wynn

   Queensland University of Technology

   Affiliation as printed

   Queensland University of Technology,Brisbane,Australia

4. Sander J. J. Leemans Aachen

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University & Fraunhofer FIT,Aachen,Germany