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Alignment Approximation for Process Trees

: Schuster, D.; Zelst, S. van; Aalst, W.M.P. van der


Leemans, S.:
Process Mining Workshops 2020 : ICPM 2020 International Workshops Padua, Italy, October 5-8, 2020. Revised Selected Papers
Cham: Springer Nature, 2021 (Lecture Notes in Business Information Processing 406)
ISBN: 978-3-030-72692-8 (Print)
ISBN: 978-3-030-72693-5 (Online)
ISBN: 978-3-030-72694-2
International Conference on Process Mining (ICPM) <2020, Online>
International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) <5, 2020, Online>
Fraunhofer FIT ()

Comparing observed behavior (event data generated during process executions) with modeled behavior (process models), is an essential step in process mining analyses. Alignments are the de-facto standard technique for calculating conformance checking statistics. However, the calculation of alignments is computationally complex since a shortest path problem must be solved on a state space which grows non-linearly with the size of the model and the observed behavior, leading to the well-known state space explosion problem. In this paper, we present a novel framework to approximate alignments on process trees by exploiting their hierarchical structure. Process trees are an important process model formalism used by state-of-the-art process mining techniques such as the inductive mining approaches. Our approach exploits structural properties of a given process tree and splits the alignment computation problem into smaller sub-problems. Finally, sub-results are composed to obtain an alignment. Our experiments show that our approach provides a good balance between accuracy and computation time.