Multi-Objective Optimization for Automated Business Process Discovery

Mohamed Ghazal, Samy Ghoniemy, Mostafa Salama

Abstract

Process Mining is a research field that aims to develop new techniques to discover, monitor and improve real processes by extracting knowledge from event logs. This relatively young research discipline has evidenced efficacy in various applications, especially in application domains where a dynamic behavior needs to be related to process models. Process Model Discovery is presumably the most important task in Process Mining since the discovered models can be used as an objective starting points for any further process analysis to be conducted. There are various quality dimensions the model should consider during discovery such as Replay-Fitness, Precision, Generalization, and Simplicity. It becomes evident that Process Model Discovery, with its current given settings, is a Multi-Objective Optimization Problem. However, most existing techniques does not approach the problem as a Multi-Objective Optimization Problem. Therefore, in this work we propose the use of one of the most robust and widely used Multi-Objective Optimizers in Process Model Discovery, the NSGA-II algorithm. Experimental results on a real life event log shows that the proposed technique outperforms existing techniques in various aspects. Also this work tries to establish a benchmarking system for comparing results of Multi-Objective Optimization based Process Model Discovery techniques.

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