Andrea Burattin
Associate Professor
Technical University of Denmark
I-PALIA: Discovering BPMN Processes with Duplicated Activities for Healthcare Domains
C. Fernández-Llatas, A. Burattin
Abstract
Process mining encompasses a range of methods designed to analyze event logs. Among these methods, control-flow discovery algorithms are particularly significant, as they enable the identification of real-world process models, known as in-vivo processes, in contrast to anticipated models. An obstacle faced by control-flow discovery algorithms is their limited ability to recognize duplicated activities, which are activities that occur in multiple locations within a process. This issue is particularly relevant in the healthcare sector, where numerous instances of duplicated activities exist in processes but remain undetected by conventional algorithms. This article introduces a novel concept for a control-flow discovery algorithm capable of effectively revealing duplicated activities. The effectiveness of this technique is demonstrated through experimentation on a synthetic dataset. Moreover, the algorithm has been implemented and its source code is available as open-source software, accessible both as a ProM plugin and a Java Maven dependency.
Paper Information and Files
In Proceedings of ICPM Workshop (PODS4H), (2023).
- DOI: 10.1007/978-3-031-56107-8_19
- BibTeX: see at DBLP
- Download text
- Best Paper at the Sixth International Workshop on Process-Oriented Data Science for Healthcare (PODS4H)
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