Authors: Sonia Deshmukh; Manoj Agarwal; Shikha Gupta; Naveen Kumar
Addresses: Department of Computer Science, University of Delhi, Delhi, India ' Hans Raj College, University of Delhi, Delhi, India ' Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India ' Department of Computer Science, University of Delhi, Delhi, India
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.
Keywords: process discovery; evolutionary algorithms; Pareto-front; multi-objective optimisation; process model quality dimensions; PAES; SPEA-II; NSGA-II; completeness; generalisation.
International Journal of Computational Science and Engineering, 2020 Vol.21 No.3, pp.446 - 456
Received: 02 Jun 2018
Accepted: 03 Jan 2019
Published online: 13 Mar 2020 *