Scientific paper resulting from the work of Rafael Gaspar de Sousa. Published at the 36th Annual ACM Symposium on Applied Computing and presented by Rafael in an online session. The paper features collaboration from researchers Prof. Dr. Sarajane Marques Peres (Rafael’s advisor), Prof. Dr. Marcelo Fantinato, and Prof. Dr. ir. Hajo A. Reijers – University of Utrecht – who served as Rafael’s co-supervisor.
Abstract: Business processes are subject to changes over time due to the need for adaptation and flexibility to a complex environment. Detecting drift as soon as possible and identifying the process elements involved, lead to a much better understanding of the process behavior, which can be a competitive edge for businesses. However, most existing approaches focus on each of these two tasks separately. Isolated approaches do not always have interfaces between them that allow you to combine solutions effectively for each corresponding task. In such cases, using the two isolated solutions together is neither feasible nor even useful from the point of view of a business analyst. This paper proposes an integrated approach to detect and locate concept drifts based on an online setting for trace clustering. Experiments with synthetic event logs with different types of control-flow changes showed that concept drifts can be detected and located efficiently.
Complete paper here (ACM Digital Library).
Reference: Rafael Gaspar de Sousa, Sarajane Marques Peres, Marcelo Fantinato, and Hajo Alexander Reijers. 2021. Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC’21). Association for Computing Machinery, New York, NY, USA, 364–373. https://doi.org/10.1145/3412841.3441918
@inproceedings{Sousa2021,
author = {de Sousa, Rafael Gaspar and Peres, Sarajane Marques and Fantinato, Marcelo and Reijers, Hajo Alexander},
title = {Concept Drift Detection and Localization in Process Mining: An Integrated and Efficient Approach Enabled by Trace Clustering},
year = {2021},
isbn = {9781450381048},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3412841.3441918},
doi = {10.1145/3412841.3441918},
booktitle = {Proceedings of the 36th Annual ACM Symposium on Applied Computing},
pages = {364–373},
numpages = {10},
keywords = {trace clustering, data mining, business processes, process mining, concept drift},
location = {Virtual Event, Republic of Korea},
series = {SAC’21}
Artigo científico resultante do trabalho de Rafael Gaspar de Sousa. Publicado na 36th Annual ACM Symposium on Applied Computing e apresentado pelo Rafael em sessão online. O artigo conta com a colaboração dos pesquisadores Profa. Dra. Sarajane Marques Peres (orientadora do Rafael), Prof. Dr. Marcelo Fantinato e do Prof.dr.ir. Hajo A. Reijers – University of Utrecht – co-supervisor of Rafael.
Artigo completo aqui (em inglês). (ACM Digital Library)
Referência: Rafael Gaspar de Sousa, Sarajane Marques Peres, Marcelo Fantinato, and Hajo Alexander Reijers. 2021. Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC’21). Association for Computing Machinery, New York, NY, USA, 364–373. https://doi.org/10.1145/3412841.3441918
@inproceedings{Sousa2021,
author = {de Sousa, Rafael Gaspar and Peres, Sarajane Marques and Fantinato, Marcelo and Reijers, Hajo Alexander},
title = {Concept Drift Detection and Localization in Process Mining: An Integrated and Efficient Approach Enabled by Trace Clustering},
year = {2021},
isbn = {9781450381048},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3412841.3441918},
doi = {10.1145/3412841.3441918},
booktitle = {Proceedings of the 36th Annual ACM Symposium on Applied Computing},
pages = {364–373},
numpages = {10},
keywords = {trace clustering, data mining, business processes, process mining, concept drift},
location = {Virtual Event, Republic of Korea},
series = {SAC’21}
}
