Structural Similarity Measurement of Business Process Model to Compare Heuristic and Inductive Miner Algorithms Performance in Dealing with Noise

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Abstract

Heuristic and Inductive Miner algorithms have different characteristics, properties, specializations, and performances in modeling business process. This research adopts three modules from Wang framework i.e. log generation, process mining, and similarity calculation to compare process mining algorithms performance in dealing with noise. The similarity calculation module measures structural similarity between reference model generated from standard event log, with mined model generated from noisy event log obtained with Heuristic and Inductive Miner algorithms. Noisy event log is obtained by adding 1% noise to the standard event log. Results from stuctural similarity measurement show that Inductive Miner algorithm obtain better performance in dealing with noise, especially related to material procurement process model of Cement Manufacturing and production in Pharmaceutical Industry.

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Nuritha, I., & Mahendrawathi, E. R. (2017). Structural Similarity Measurement of Business Process Model to Compare Heuristic and Inductive Miner Algorithms Performance in Dealing with Noise. In Procedia Computer Science (Vol. 124, pp. 255–263). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.12.154

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