Biazus, Miller, Habekost dos Santos, Carlos, Narumi Takeda, Larissa, Palazzo Moreira de Oliveira, José, Fantinato, Marcelo, Mendling, Jan, Heloisa Thom, Lucinéia. 2019. Software Resource Recommendation for Process Execution Based on the Organization's Profile. In Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science, Hrsg. Hartmann S., Küng J., Chakravarthy S., Anderst-Kotsis G., Tjoa A., Khalil I. 118-128. Cham: Springer.

RIS

@INPROCEEDINGS{Biazus2019,
title = {Software Resource Recommendation for Process Execution Based on the Organization's Profile.},
author = {Miller Biazus and Carlos Habekost dos Santos and Larissa Narumi Takeda and José Palazzo Moreira de Oliveira and Marcelo Fantinato and Jan Mendling and Lucinéia Heloisa Thom},
publisher = {Springer},
year = {2019},
isbn = {978-3-030-27617-1},
address = {Cham},
url = {https://link.springer.com/chapter/10.1007%2F978-3-030-27618-8_9},
publisher = {Springer},
language = {EN},
pages = {118-128},
series = {Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science},
abstract = {Lack of information on the infrastructure resources needed to execute business processes may interfere with the execution flow of the BPM lifecycle phases. If an organization recognizes that it does not have the resources needed to execute a process as planned, it might have to redesign the process. This paper presents an approach to recommending the infrastructure resources needed to execute a process. The recommendation relies on the task labels of the process model and comprises two phases: resource type classification and resource recommendation. The approach contributes to the redesign phase as it provides the process analyst with information on the resources needed to execute the process. It also supports decision-making process before the implementation phase regarding, for example, remodeling, project cancellation, resource procurement etc. The developed approach was validated based on a set of real processes of a public university through a cross-fold validation that reached 83% of accuracy.},
}