Petri net‐based deviation detection between a process model with loop semantics and event logs

Abstract

Summary Information systems have been widely used to realize business processes. The business processes can be described by formal or semi‐formal models. In practice, there are a number of realistic processes involving loop semantics. As an insufficient design or improvement of the information system, event logs generated from these systems may not be consistent with the existing models. By comparing an existing process model with the event logs, we can detect inconsistencies called deviations and verify and extend the business process model. In this paper, some abnormal activities in business processes are formally defined based on Petri nets. An efficient approach to detect deviations between the process model with loop structures and event logs is proposed. A clinical process in a healthcare information system is used as a case study to illustrate our work. Experimental results show the effectiveness and efficiency of the presented approach.

A Model Repair Approach Based on Petri Nets by Constructing Free-Loop Structures

“…In our previous work, we define a structure called freeloop structure [24], [25]. We propose two methods related to the free-loop structures: one is a conformance checking method [24], and another is a process discovery algorithm [25]. Transitions in free loop structures can fire any times in a process model [25].…”

High performance deep learning techniques for big data analytics

“…Petri Nets are also used for supervised and unsupervised learning . In the works of Tian et al and Wang et al, Petri nets are optimized for learning activities from business processes.…”