Scientific paper resulting from the elaboration of a new algorithm for process model discovery, titled “X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method”. Published at the 25th IEEE International Enterprise Distributed Object Computing Conference and presented by Marcelo Fantinato in an online session. The algorithm was modeled mainly by Prof. Dr. Marcelo Fantinato, with the aid of Prof. Dr. Sarajane Marques Peres (for Genetic Algorithm principles) and the support of Prof. Dr. ir. Hajo A. Reijers (University of Utrecht).
The new algorithm, called X-processes, is capable of achieving good results for process model discovery, considering different event logs and quality measures such as completeness and precision.
Abstract: 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.
Complete paper here (IEEE Xplore).
Reference: M. Fantinato, S. M. Peres and H. A. Reijers, “X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method,” 2021 IEEE 25th International Enterprise Distributed Object Computing Conference (EDOC), Gold Coast, Australia, 2021, pp. 114-123, doi: 10.1109/EDOC52215.2021.00022.
@INPROCEEDINGS{Fantinato2021,
author={Fantinato, Marcelo and Peres, Sarajane Marques and Reijers, Hajo A.},
booktitle={2021 IEEE 25th International Enterprise Distributed Object Computing Conference (EDOC)},
title={X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method},
year={2021},
pages={114-123},
doi={10.1109/EDOC52215.2021.00022}}
Artigo científico resultante da elaboração de um novo algoritmo para descoberta de modelo de processo, intitulado “X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method”. Publicano no 25th IEEE International Enterprise Distributed Object Computing Conference e apresentado por Marcelo Fantinato em uma sessão online. O algoritmo foi modelado principalmente pelo Prof. Dr. Marcelo Fantinato, com a ajuda da Profa. Dra. Sarajane Marques Peres (para questões relacionadas a Algoritmos Genéticos) e com o suporte do Prof. Dr. ir. Hajo A. Reijers (University of Utrecht).
O novo algoritmo, chamado X-processes, é capaz de atingir bons resultados para descoberta de modelos de processo, considerando diferentes logs de eventos e medidas de qualidade como completude e precisão.
Artigo completo aqui – em ingles. (IEEE Xplore)
Referência: M. Fantinato, S. M. Peres and H. A. Reijers, “X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method,” 2021 IEEE 25th International Enterprise Distributed Object Computing Conference (EDOC), Gold Coast, Australia, 2021, pp. 114-123, doi: 10.1109/EDOC52215.2021.00022.
@INPROCEEDINGS{Fantinato2021,
author={Fantinato, Marcelo and Peres, Sarajane Marques and Reijers, Hajo A.},
booktitle={2021 IEEE 25th International Enterprise Distributed Object Computing Conference (EDOC)},
title={X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method},
year={2021},
pages={114-123},
doi={10.1109/EDOC52215.2021.00022}}
