X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method

Autor(es)

Fantinato, Marcelo ; Peres, Sarajane Marques ; Reijers, Hajo A. ; IEEE COMP SOC

• Tipo de documento: Artigo Científico
• Fonte: 2021 IEEE 25TH INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE (EDOC 2021); v. N/A, p. 10-pg., 2021-01-01.

Resumo

Although process model discovery has been extensively investigated over the past two decades, existing discovery methods are still not considered fully satisfactory. One problem is the difficulty of discovering accurate process models, achievable with both high recall (or fitness) and high precision, particularly for real-world event logs. This paper introduces a process discovery method, namely X-Processes, based on genetic algorithms, which aims to optimize accuracy through the F-Score calculated between recall and precision. Although genetic algorithms have been used to discover process models, such methods also have limitations as do other non-genetic algorithms-based methods. Experimental results for 12 real-world event logs show the accuracy of the process models discovered by X-Processes is higher than those of six other state-of-the-art discovery methods, including one also based on genetic algorithms. Besides accuracy, X-Processes delivers sound process models. Although its execution time is longer than the other compared discovery methods, X-Processes emerges as a solution when the need for a highly accurate process model outweighs the hunger for agility. (AU)

• Processo FAPESP: 17/26491-1 - Uma abordagem evolutiva para a descoberta de processos de negócio não estruturados com base em coevolução cooperativa e no modelo de ilha
• Beneficiário: Marcelo Fantinato
• Modalidade de apoio: Bolsas no Exterior - Pesquisa
• Processo FAPESP: 20/05248-4 - Aprimoramento do algoritmo X-Processes para descobrir modelos de processo mais precisos
• Beneficiário: Marcelo Fantinato
• Modalidade de apoio: Auxílio à Pesquisa - Regular