TY - GEN
T1 - GigMaleBPMN
T2 - 4th International Conference on Information Systems and Software Technologies, ICI2ST 2023
AU - De La Cruz, Marco Antonio
AU - Estrada, Luis Miguel
AU - Díaz, Eduardo
AU - Panach, José Ignacio
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The BPMN model allows organizations to depict business processes. However, this model does not capture the functional behavior of the system to generate graphical components. This article proposes a method for generating graphical components from a BPMN model using Machine Learning. The method is structured in four steps: (1) creating a BPMN model, (2) using Machine Learning to identify the elements of the BPMN model and indicate which graphical components should be used, (3) manually developing wireframes in Balsamiq based on the identified BPMN elements, and (4) using Machine Learning to identify the graphical components of the wireframes, enabling the automatic generation of graphical components and code. To enhance understanding, an illustrative example was developed using the method. The results prove that this approach allows for the automatic generation of graphical components using Machine Learning from a BPMN model.
AB - The BPMN model allows organizations to depict business processes. However, this model does not capture the functional behavior of the system to generate graphical components. This article proposes a method for generating graphical components from a BPMN model using Machine Learning. The method is structured in four steps: (1) creating a BPMN model, (2) using Machine Learning to identify the elements of the BPMN model and indicate which graphical components should be used, (3) manually developing wireframes in Balsamiq based on the identified BPMN elements, and (4) using Machine Learning to identify the graphical components of the wireframes, enabling the automatic generation of graphical components and code. To enhance understanding, an illustrative example was developed using the method. The results prove that this approach allows for the automatic generation of graphical components using Machine Learning from a BPMN model.
KW - BPMN
KW - Graphics components
KW - Machine Learning
KW - Wireframes
UR - https://www.scopus.com/pages/publications/85200750870
U2 - 10.1109/ICI2ST62251.2023.00021
DO - 10.1109/ICI2ST62251.2023.00021
M3 - Contribución a la conferencia
AN - SCOPUS:85200750870
T3 - Proceedings - 2023 4th International Conference on Information Systems and Software Technologies, ICI2ST 2023
SP - 97
EP - 103
BT - Proceedings - 2023 4th International Conference on Information Systems and Software Technologies, ICI2ST 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 November 2023 through 24 November 2023
ER -