# Addressing Business Process Deviations through the Evaluation of Alternative Pattern-Based Models
## 2 Related Work  
As stated in the introduction, in recent years, there is growing attention towards process-aware information systems, which are designed to aid in managing, supervising, and overseeing business processes. [1]. Business process management (BPM) is a well-known method that encompasses a set of organized and interconnected tasks or activities aimed at delivering a distinct service or product to one or multiple customers. It is commonly represented as a flowchart illustrating the sequential progression of its activities [2]. The Integrated Computer-Aided Manufacturing (ICAM) program, starting in the 1970s, recognizes the need for enhanced analysis and communication techniques among individuals working to improve manufacturing productivity. As a result, the program introduced a set of modelling approaches known as the ICAM definition (IDEF) methods [3]. Since the 1980s, scholars have strived to develop standardized and cohesive methods to represent and quantify processes [4] such as the structured analysis and design technique (SADT) (top-down approach) [5]. The Business Process Model and Notation (BPMN) was first developed by the Business Process Management Initiative (BPMI), which made version 1.0 available to the public in May 2004. Following the merger of BPMI with the Object Management Group (OMG) in June 2005 [6], the OMG published a BPMN specification document in February 2006 [7]. The strength of the BPMN resides in two important aspects: (i) simplicity, which is due to the abstraction level provided by the standard; and (ii) the possibility of being automatically translated into a business execution language and, then, to generate a machine-readable prototype of business processes [8]. BPMN was created with the explicit purpose of bridging the gap between the business and technical perspectives regarding processes [9; 10]. In BPMN, tokens serve as a theoretical concept to define a process's behaviour, with elements interacting as the token moves through the process. However, the specific details of what a token contains are not described in the official BPMN specification [11]. It is common to associate tokens with details such as process instance ID, data, flow object ID, timestamp, and state information. The data held by a token can differ depending on the specific case and implementation [12]. In contemporary organizations, as BPs are becoming increasingly complex, often involving thousands of activities that need to be modelled to control their complexity. Over-specifying the models to handle all foreseen situations and repair anomalies has been common practice in industry to overcome unexpected malfunctions. However, this approach is expensive and still cannot account for all possible situations, as evidenced by the COVID-19 pandemic. Therefore, there is a pressing need to repair the model to sustain business operations. [PERSON] et al. [13] addressed the recurring issue of developer deviations from the process model in the multi-viewpoint-based development of complex systems. These deviations, which can be behavioural or structural in nature, arise without proper methodological support. The authors proposed a solution to overcome these issues through a viewpoint-based development process. Earlier research has addressed four key areas related to the correction of deviations in business models, namely: problem identification, solution evaluation, model validation and model reparation. Although, detecting deviations is not the subject of this study, and in this paper, it is supposed that an anomaly detection has been flagged and a repairing mechanism is proposed, numerous studies, and organizations such as Airbus have focused on the aspect of problem identification in the One-Way project [14], which established a Numerical Twin to identify any deviations in a system from its intended behaviour, whereas other studies have considered artificial intelligence (AI) to improve the system's ability to identify and describe systematic deviations present in data and models [15; 16]. [PERSON] et al.