Process model comparison can be exploited to assess the quality of organizational procedures, to identify non-conformances with respect to given standards, and to highlight critical situations. Sometimes, however, it is difficult to make sense of large and complex process models, while a more abstract view of the process would be sufficient for the comparison task. In this paper, we show how process traces, abstracted on the basis of domain knowledge, can be provided as an input to process mining, and how abstract models (i.e., models mined from abstracted traces) can then be compared and ranked, by adopting a similarity metric able to take into account penalties collected during the abstraction phase. The overall framework has been tested in the field of stroke management, where we were able to rank abstract process models more similarly to the ordering provided by a domain expert, with respect to what could be obtained when working on non-abstract ones. © 2018, Springer Nature Switzerland AG.
From Semantically Abstracted Traces to Process Mining and Process Model Comparison
Leonardi G.;Quaglini S.;Cavallini A.;Montani S.
2018-01-01
Abstract
Process model comparison can be exploited to assess the quality of organizational procedures, to identify non-conformances with respect to given standards, and to highlight critical situations. Sometimes, however, it is difficult to make sense of large and complex process models, while a more abstract view of the process would be sufficient for the comparison task. In this paper, we show how process traces, abstracted on the basis of domain knowledge, can be provided as an input to process mining, and how abstract models (i.e., models mined from abstracted traces) can then be compared and ranked, by adopting a similarity metric able to take into account penalties collected during the abstraction phase. The overall framework has been tested in the field of stroke management, where we were able to rank abstract process models more similarly to the ordering provided by a domain expert, with respect to what could be obtained when working on non-abstract ones. © 2018, Springer Nature Switzerland AG.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
