X-Processes: Process model discovery with the best balance among fitness, precision, simplicity, and generalization through a genetic algorithm

• Published in: Information systems (Oxford) Vol. 119; p. 102247
• Main Authors: Fantinato, Marcelo, Peres, Sarajane Marques, Reijers, Hajo A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023
• Subjects: Accuracy; Automated process discovery; Fitness; Generalization; Genetic algorithms; Precision; Process mining; Recall; Simplicity; Process mining; Accuracy; Precision; Recall; Automated process discovery; Generalization; Simplicity; Genetic algorithms; Fitness
• ISSN: 0306-4379; 1873-6076
• DOI: 10.1016/j.is.2023.102247

Although process model discovery has been largely investigated over the past two decades, existing process discovery methods are not yet considered fully satisfactory. A particular issue is the difficulty of discovering process models with a good balance among recall (or fitness), precision, generalization, and simplicity, notably for real-world event logs. This article revisits the genetic algorithms-based X-Processes process discovery method, discussing the results of using a fitness function calculated through a harmonic mean that considers the four main metrics of process model quality used in process mining — recall, precision, generalization and simplicity. Although genetic algorithms have been used to discover process models, such methods have limitations, as do other non-genetic algorithm-based methods. X-Processes was tested with two harmonic mean options — one based on recall and precision only and another based on the four quality metrics. X-Processes showed superior effectiveness when compared to state-of-the-art process discovery methods, including one also based on genetic algorithms. The experimental results obtained for both harmonic mean options, using 12 real-world event logs, show that the overall quality of all process models discovered by X-Processes is superior to that of the baseline methods. While its execution time is longer than the other compared process discovery methods, X-Processes emerges as a solution when the need for a higher quality process model outweighs the hunger for agility.