The undergraduate student Mateus Alex dos Santos Luna presented his paper titled “Vector Space Models for Trace Clustering: A Comparative Study” at the ENIAC 2021 conference and received the second-place award among the best undergraduate student papers of the event. The work also benefited from the collaboration of researchers: MSc. Thais Rodrigues Neubauer, MSc. Andre Lima, Prof. Dr. Marcelo Fantinato, and advisor Prof. Dr. Sarajane Peres.
Read the abstract of the paper to learn a little more about Mateus’s work: Process mining explores event logs to offer valuable insights to business process managers. Some types of business processes are hard to mine, including unstructured and knowledge-intensive processes. Then, trace clustering is usually applied to event logs aiming to break it into sublogs, making it more amenable to the typical process mining task. However, applying clustering algorithms involves decisions, such as how traces are represented, that can lead to better results. In this paper, we compare four vector space models for trace clustering, using them with an agglomerative clustering algorithm in synthetic and real-world event logs. Our analyses suggest the embeddings-based vector space model can properly handle trace clustering in unstructured processes.
If you would like to access the full article, click here.
Mateus was part of the CNPq’s scientific initiation program and thanks the agency for the support provided during the research process.
Reference: Luna, M., Lima, A., Neubauer, T., Fantinato, M., & Peres, S. (2021). Vector space models for trace clustering: a comparative study. In Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional, (pp. 446-457). Porto Alegre: SBC. doi:10.5753/eniac.2021.18274
Bibtex:
@inproceedings{Luna2021,
author = {Mateus Luna and André Lima and Thaís Neubauer and Marcelo Fantinato and Sarajane Peres},
title = {Vector space models for trace clustering: a comparative study},
booktitle = {Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional},
location = {Evento Online},
year = {2021},
keywords = {},
issn = {2763-9061},
pages = {446–457},
publisher = {SBC},
address = {Porto Alegre, RS, Brasil},
doi = {10.5753/eniac.2021.18274},
url = {https://sol.sbc.org.br/index.php/eniac/article/view/18274}
}
O aluno de graduação Mateus Alex dos Santos Luna apresentou seu artigo intitulado “Vector space models for trace clustering: a comparative study” na conferência ENIAC 2021, e recebeu o prêmio de segundo lugar entre os melhores artigos de alunos de graduação do evento. O trabalho também contou com a colaboração dos pesquisadores: MSc. Thais Rodrigues Neubauer, MSc. Andre Lima, Prof. Dr. Marcelo Fantinato e da orientadora Profa. Dra. Sarajane Peres.
Se quiser acessar o artigo na íntegra, clique aqui. (em inglês)
O Mateus fez parte do programa de iniciação científica do CNPq e agrade a agência pelo suporte fornecido durante a realização da pesquisa.
Referência: Luna, M., Lima, A., Neubauer, T., Fantinato, M., & Peres, S. (2021). Vector space models for trace clustering: a comparative study. In Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional, (pp. 446-457). Porto Alegre: SBC. doi:10.5753/eniac.2021.18274
Bibtex:
@inproceedings{Luna2021,
author = {Mateus Luna and André Lima and Thaís Neubauer and Marcelo Fantinato and Sarajane Peres},
title = {Vector space models for trace clustering: a comparative study},
booktitle = {Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional},
location = {Evento Online},
year = {2021},
keywords = {},
issn = {2763-9061},
pages = {446–457},
publisher = {SBC},
address = {Porto Alegre, RS, Brasil},
doi = {10.5753/eniac.2021.18274},
url = {https://sol.sbc.org.br/index.php/eniac/article/view/18274}
}
