Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Towards Action + State Process Model Discovery

Alessio Bottrighi
ORCID logo ,
Marco Guazzone
ORCID logo ,
Giorgio Leonardi
,
Stefania Montani
ORCID logo ,
Manuel Striani
* ORCID logo ,
Paolo Terenziani
Version 1 : Received: 28 June 2023 / Approved: 28 June 2023 / Online: 28 June 2023 (16:19:00 CEST)

A peer-reviewed article of this Preprint also exists.

Bottrighi, A.; Guazzone, M.; Leonardi, G.; Montani, S.; Striani, M.; Terenziani, P. Towards Action-State Process Model Discovery. Data 2023, 8, 130. Bottrighi, A.; Guazzone, M.; Leonardi, G.; Montani, S.; Striani, M.; Terenziani, P. Towards Action-State Process Model Discovery. Data 2023, 8, 130.

Abstract

Process model discovery covers different methodologies to mine a process model from traces of process executions, and is gaining an important role in Artificial Intelligence research. Current approaches in the area, with few exceptions, focus on determining a model of the flow of actions only. However, in several contexts, (i) restricting the attention to actions is quite limitative, since the effects of such actions have to be analysed, too, and (ii) traces provide additional pieces of information, in the form of states (i.e., values of parameters possibly affected by the actions): for instance, in several medical domains traces include both actions and measurements of patients’ parameters. In this paper, we propose AS-SIM (Action-State SIM), the first approach able to mine a process model which comprehends two distinct classes of nodes, to capture both actions and states.

Keywords

Process Mining; Process Model Discovery; Mining action+state evolution

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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