Introduction

Business process management (BPM) is the art and science of overseeing how work is performed in an organization to ensure consistent outcomes and to take advantage of improvement opportunities (Dumas et al., 2013). Typically, these improvement opportunities include reductions in cost and execution times, enhanced quality and efficiency, as well as better productivity of processes (Arias et al., 2015). In recent years, the use of information systems in different organizations has increased, thereby facilitating the storage of information relating to the activities that are executed in distinct processes (e.g. case ID, activity name, timestamp, resource) in event logs. This information, also known as event data, can be used to improve end-to-end processes (van der Aalst, 2016). Accordingly, there is an emerging discipline, called process mining, which focuses on extracting useful knowledge based on the information stored in the event logs (van der Aalst, 2016). Process mining can be seen as a means to bridge the gap between Data Science and Process Science, where Data science refers to an interdisciplinary field that aims to extract real value from data, and Process Science refers to a broader discipline that combines knowledge from information technology and management sciences to improve and run operational processes (van der Aalst, 2016). Both BPM and process mining are interested in profoundly analyzing business processes. In conjunction with the methods, techniques and tools created for the design, execution and analysis of operational business processes (van der Aalst, 2013), there is also a central aspect to consider within BPM and process mining: the resource perspective (Dumas et al., 2013), also known as the organizational perspective (van der Aalst, 2016). This perspective focuses on the analysis of information related to the resources that are in charge of executing the activities of a business process (e.g. human resources, software systems, and equipment, among others) (Dumas et al., 2013). This helps to generate insights into how the resources work and it facilitates a more in-depth study of their behavior regarding the processes (Guo et al., 2013; Huang et al., 2012a). In particular, human resource allocation has been considered as a significant problem within the context of BPM (Huang et al., 2012b; Wibisono et al., 2015; Xu et al., 2008; Zhao and Zhao, 2014), due to the influence that the correct allocation may have on the performance of the process (Liu et al., 2014; Zhao and Zhao, 2014), on costs (Huang et al., 2011; Obregon et al., 2013), and on the efficient use of resources during the process execution (Fadol et al., 2015; Kumar et al., 2002; Xu et al., 2008).