Scientific paper resulting from the work of Thaís Rodrigues Neubauer and Glaucia Pamponet Sobrinho. Published at the 5th IEEE Workshop on Human-in-the-Loop Methods and Future of Work in BigData (IEEE International Conference on Big Data), titled “Visualization for enabling human-in-the-loop in trace clustering-based process mining tasks” and presented by Thaís in a online session. The paper features collaboration from researchers Prof. Dr. Marcelo Fantinato and Prof. Dr. Sarajane Marques Peres (Thais and Glaucia’s advisor).
Abstract: Process mining encompasses a series of tasks aimed at discovering knowledge about business processes from event logs underlying information systems deployed in organizations. Considering real-world business processes, high-complexity issues often prevent process mining techniques from producing satisfactory results. Business processes’ complexity arises from: (i) high behavioral variability as presented in unstructured processes, e.g. knowledge intensive processes, in which decisions commonly dependent on human actions; (ii) data volume, as it can reach big data levels in organizations with high-volume operations. Trace clustering can support mitigating high-complexity related issues. The process instance profiles resulting from trace clustering divide a complex problem into smaller and simpler ones. However, interpreting clustering results frequently requires decision-making and reasoning that might benefit from domain experts’ knowledge. Especially, in trace clustering-based process mining tasks, domain experts involvement enable results evaluation from the business process perspective. In this paper, a proposal for a trace clustering results visualization is presented. This visualization strategy supports evaluation from a business process perspective, enabling human-in-the-loop strategies. In order to illustrate the usefulness and appropriateness of the visualization, we present three use cases modeled on real-world event logs.
Complete paper here (IEEE Xplore).
Reference: T. R. Neubauer, G. Pamponet Sobrinho, M. Fantinato and S. M. Peres, “Visualization for enabling human-in-the-loop in trace clustering-based process mining tasks,” 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 3548-3556, doi: 10.1109/BigData52589.2021.9671985.
Bibtex:
@INPROCEEDINGS{Neubauer2021 ,
author={Neubauer, Thais Rodrigues and Pamponet Sobrinho, Glaucia and Fantinato, Marcelo and Peres, Sarajane Marques},
booktitle={2021 IEEE International Conference on Big Data (Big Data)},
title={Visualization for enabling human-in-the-loop in trace clustering-based process mining tasks},
year={2021},
pages={3548-3556},
doi={10.1109/BigData52589.2021.9671985}}
Artigo científico resultante do trabalho de Thaís Rodrigues Neubauer e Glaucia Pamponet Sobrinho. Publicado no 5th IEEE Workshop on Human-in-the-Loop Methods and Future of Work in BigData (IEEE International Conference on Big Data), intitulado “Visualization for enabling human-in-the-loop in trace clustering-based process mining tasks” e apresentado por Thais em uma sessão online. O paper conta com a colaboração de Prof. Dr. Marcelo Fantinato and Profa. Dra. Sarajane Marques Peres (orientadora).
Artigo completo aqui – em inglês (IEEE Xplore).
Referência: T. R. Neubauer, G. Pamponet Sobrinho, M. Fantinato and S. M. Peres, “Visualization for enabling human-in-the-loop in trace clustering-based process mining tasks,” 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 3548-3556, doi: 10.1109/BigData52589.2021.9671985.
Bibtex:
@INPROCEEDINGS{Neubauer2021,
author={Neubauer, Thais Rodrigues and Pamponet Sobrinho, Glaucia and Fantinato, Marcelo and Peres, Sarajane Marques},
booktitle={2021 IEEE International Conference on Big Data (Big Data)},
title={Visualization for enabling human-in-the-loop in trace clustering-based process mining tasks},
year={2021},
pages={3548-3556},
doi={10.1109/BigData52589.2021.9671985}}
