MOEA for discovering Pareto-optimal process models: an experimental comparison. (13th March 2020)

• Record Type: Journal Article
• Authors: Deshmukh, Sonia; Agarwal, Manoj; Gupta, Shikha; Kumar, Naveen
• Is Part Of: International journal of computational science and engineering. Volume 21:Number 3(2020)
• Page Start: 446
• Page End: 456
• Publication Date: 2020-03-13
• Subjects: process discovery -- evolutionary algorithms -- Pareto-front -- multi-objective optimisation -- process model quality dimensions -- PAES -- SPEA-II -- NSGA-II -- completeness -- generalisation; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Process mining aims at discovering the workflow of a process from the event logs that provide insights into organisational processes for improving these processes and their support systems. Process mining abstracts the complex real-life datasets into a well-structured form known as a process model. In an ideal scenario, a process mining algorithm should produce a model that is simple, precise, general and fits the available logs. A conventional process mining algorithm typically generates a single process model that may not describe the recorded behaviour effectively. Multi-objective evolutionary algorithms (MOEA) for process mining optimise two or more objectives to generate several competing process models from the event logs. Subsequently, a user can choose a model based on his/her preference. In this paper, we have experimentally compared the popular second-generation MOEA algorithms for process mining.