Research Article
Comparative Analysis of Process Mining Algorithms in Python
@INPROCEEDINGS{10.1007/978-3-030-91421-9_3, author={Andr\^{e} Filipe Domingos Gomes and Ana Cristina Wanzeller Guedes de Lacerda and Joana Rita da Silva Fialho}, title={Comparative Analysis of Process Mining Algorithms in Python}, proceedings={Smart Objects and Technologies for Social Good. 7th EAI International Conference, GOODTECHS 2021, Virtual Event, September 15--17, 2021, Proceedings}, proceedings_a={GOODTECHS}, year={2022}, month={1}, keywords={Big data in healthcare PM4Py Process mining Process discovery Conformance checking}, doi={10.1007/978-3-030-91421-9_3} }- André Filipe Domingos Gomes
Ana Cristina Wanzeller Guedes de Lacerda
Joana Rita da Silva Fialho
Year: 2022
Comparative Analysis of Process Mining Algorithms in Python
GOODTECHS
Springer
DOI: 10.1007/978-3-030-91421-9_3
Abstract
In many sectors, there is a large amount of data collected and stored, which is not analyzed. The health area is a good example. This situation is not desirable, as the data can provide historical information or trends that may help to improve organizations performance in the future. Process mining allows the extraction of knowledge from data generated and stored in the information systems.
This work aims to contribute to the aforementioned knowledge extraction, comparing different algorithms in process mining techniques, using health care processes and data. The results showed that Inductive Miner and Heuristic Miner are the algorithms with better results. Considering the execution times, Petri Net is the type of model that takes longer, but it is the one that allows a better analysis.
