| Full text | |
| Author(s): |
Faria Junior, Elio Ribeiro
;
Neubauer, Thais Rodrigues
;
Fantinato, Marcelo
;
Peres, Sarajane Marques
;
Montali, M
;
Senderovich, A
;
Weidlich, M
Total Authors: 7
|
| Document type: | Journal article |
| Source: | PROCESS MINING WORKSHOPS, ICPM 2022; v. 468, p. 13-pg., 2023-01-01. |
| Abstract | |
Object-centric event log is a format for properly organizing information from different views of a business process into an event log. The novelty in such a format is the association of events with objects, which allows different notions of cases to be analyzed. The addition of new features has brought an increase in complexity. Clustering analysis can ease this complexity by enabling the analysis to be guided by process behaviour profiles. However, identifying which features describe the singularity of each profile is a challenge. In this paper, we present an exploratory study in which we mine frequent patterns on top of clustering analysis as a mechanism for profile characterization. In our study, clustering analysis is applied in a trace clustering fashion over a vector representation for a flattened event log extracted from an object-centric event log, using a unique case notion. Then, frequent patterns are discovered in the event sublogs associated with clusters and organized according to that original object-centric event log. The results obtained in preliminary experiments show association rules reveal more evident behaviours in certain profiles. Despite the process underlying each cluster may contain the same elements (activities and transitions), the behaviour trends show the relationships between such elements are supposed to be different. The observations depicted in our analysis make room to search for subtler knowledge about the business process under scrutiny. (AU) | |
| FAPESP's process: | 20/05248-4 - Enhancing the X-Processes algorithm to discover more accurate process models |
| Grantee: | Marcelo Fantinato |
| Support Opportunities: | Regular Research Grants |