## 2 Related Work  
The majority of research in the area of configurable process models has addressed the issue of describing a configurable process model, or the issue of obtaining such a configurable process model [10; 11; 12; 13; 14; 15]. Manual (i.e., by the user) process model configuration has been addressed by [10; 16].  
Figure 2: Overview of the proposed framework to derive a process model. Three alternative derivation strategies allow obtaining a configuration that later on is used to derive a process model from the configurable process model.  
In [16], for example, a questionnaire-driven approach for configuring a reference model is taken, guiding the user in defining a configuration. The work by [PERSON] et al. [10] uses a configurable tree-like representation, which is sound by construction. Applied in the Configurable Services for Local Governments (CoSeLoG) project [17], this approach merges variants of different municipalities to create a configurable process model. The same author underlines in [5] the difficulty of creating a configuration since the user needs a high abstraction level about the process. Hence, the author uses a meta-model to automatically construct an abstraction that helps the end user to apply configurations. The same author has also extended the work to consider several qualitative process aspects such as performance, cost and satisfaction indicators. The results are then presented to the end user, who then inspects the proposed configurations and selects one to be applied.  
A configurable process model allows a reference model to be adapted to different business scenarios where the process is being executed (e.g., different branches of a large organization or different companies belonging to a corporate group). A complementary approach aims at adapting a general process model to different modeling views that are needed for the integrated modeling of business processes and information systems [18; 19].  
However, event data are not often used to configure a configurable process model. One of the few approaches using this is the Evolutionary Tree Miner (ETM) [20], which is able to discover a process model including configurations, when given multiple event logs. However, the performance of the ETM is not optimal. Because it is an evolutionary algorithm and the configuration aspect adds many possibilities, the challenge of discovering a process model and a configuration becomes challenging.  
The main limitation of existing approaches is the restricted number of configurations a configurable process model can have. The approach proposed in this paper aims to enhance the work already done in configurable process models by allowing a large number of configurations and by providing a framework that allows combining a configurable process model and an event log to derive a process model that better represents the observed behavior in the event log. Similar to existing approaches, we implemented our techniques in the process mining framework (ProM).  
Theoretical Foundation of the Framework for Deriving a Process Model from a Configurable Process Model  
In this section, we introduce the theoretical foundation of the proposed framework, such as event log, process tree and configurable process tree, and how to measure the quality of a derived process tree to represent the observed behavior in an event log.