_Article_  
Strategies to Automatically Derive a Process Model  
from a Configurable Process Model Based on  
Event Data  
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The importance of having a configurable process model to describe process variants has been a subject of study in the literature. In [7], twenty Business Process Management (BPM) use cases are identified. Three of those use cases involve configurable process models: design configurable model (DesCM), merge models into configurable model (MerCM) and configure configurable model (ConCM) (see Figure 1). In particular, the use case ConCM consists of deriving a process model from a configurable process model so as to represent a particular process variant. As shown in Figure 1, the original use case does not consider the usage of other sources of information beyond manual configuration. However, in the last few years, a new discipline called process mining has emerged, which studies extracting and analyzing data recorded in information systems about processes' behavior [8]. More specifically, process discovery techniques aim at creating a process model based on the historical behavior recorded in an event log. Even though some process mining techniques have been applied to support the creation and derivation of configurable process models (e.g., [9]), the approach was simplistic and not executable on real-life data. While analyzing the literature about configurable process models, we have identified two main challenges. As mentioned before, two separate approaches can be recognized: on the one hand, automated process discovery and, on the other hand, manual configuration of configurable process models. At the same time, there is a growing availability of event data in organizations, which also puts pressure on having specialized, smart and efficient techniques to include historical data for configuring a configurable process model. Therefore, the first and main challenge is to develop an automatic method to derive a process model from a configurable model that better represents the observed behavior of a process variant (e.g., the process execution in a target branch) as stored in a given event log, which can be applied to real-life data. It is also important that configuration decisions can be defined in a local context. Therefore, a second challenge is to create a simple representation for the different degrees of freedom we want to allow in the configurable nodes [10]. The goal of automatically deriving a process model from a configurable process model that better represents the behavior observed in an event log, instead of discovering a process model only based on the behavior observed in the event log, is useful when an organization has already defined a configurable process model in order to specify a certain degree of standardization and flexibility among the different branches where the process is being performed. If a new branch is added, it would be desirable to obtain a derived process model that is as similar as possible to the  
Figure 1: Business process management (BPM) use cases [7] related to configurable process models: (**a**) manually design a configurable process model (DesCM), (**b**) merge the collection of process models to generate a configurable process model (MerCM) and (**c**) configure a configurable process model (ConCM) to obtain a process model. In this article, we propose (**d**) as a new use case: to derive a process model based on a configurable process model and using event data (ConCME). current way of executing the process at such a branch, represented by the event log. If instead, we use any process discovery technique on the event log, we will obtain a process model that describes how the process is currently executed at the branch, but that may not necessarily be compliant with the desired standardization. This article presents an approach with a three-fold objective.