| Full text | |
| Author(s): |
Fantinato, Marcelo
;
Peres, Sarajane Marques
;
Reijers, Hajo A.
Total Authors: 3
|
| Document type: | Journal article |
| Source: | INFORMATION SYSTEMS; v. 119, p. 25-pg., 2023-07-10. |
| Abstract | |
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. & COPY; 2023 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 20/05248-4 - Enhancing the X-Processes algorithm to discover more accurate process models |
| Grantee: | Marcelo Fantinato |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 17/26491-1 - An evolutionary approach to the discovery of unstructured business processes based on cooperative coevolution and the island model |
| Grantee: | Marcelo Fantinato |
| Support Opportunities: | Scholarships abroad - Research |