# Quality-Aware Resource Model Discovery
## 2 Related Works  
In the process mining discipline, automated process discovery in terms of the control-flow perspective has been a principal research stream for the last two decades [11]. Beyond the control-flow perspective, prior works using data attributes, i.e., process contexts, have received considerable interest to improve and complement the processes [3; 4; 5; 12; 13; 14; 15; 16; 17; 18]. In other words, context-aware process mining approaches have covered a range of research themes, including process discovery, conformance checking, and enhancement [3; 4; 5; 12; 13; 14; 15; 16; 17; 18].  
Most context-aware process discovery efforts have followed a two-step approach: producing a process model with the control-flow and then annotating context data, e.g., time, cost, and decisions, on the model [12; 13; 14]. For example, time-related contexts, e.g., service time, bottlenecks, and utilization, were annotated on the transition systems, and this research enabled the prediction of time-related values [12]. In addition, [PERSON] and [PERSON] employed cost-related contexts in manufacturing processes, including labor costs, material costs, and overhead costs [13]. [PERSON] and [PERSON] identified the influence of case and event attributes on the choices, i.e., XOR-split, in the processes [14]. In more detail, they uncovered the rules explaining the choices based on the characteristics of cases, and those were annotated in the process model. In addition to these prior works, i.e., \"control-flow first\" approaches, there was an attempt to produce a context-aware inductive process model, which considers the control-flow and context information at the same time [3]. The authors constructed the process tree with data semantics instead of control-flow semantics.  
Regarding conformance checking, there have been attempts to compare process models and event logs in terms of both control-flow and other process contexts [15; 16; 17]. [PERSON] et al. [15] proposed a method that considers data dependencies, resource assignments, and time constraints for conformance checking. In addition, the authors suggested a customizable cost function to balance the deviations from multiple perspectives. Conformance checking with process contexts has been actively utilized in declarative process models [16; 17]. In general, declarative process models should have numerous process constraints to accomplish business goals; most process constraints are defined in multi-perspectives, and conformance checking is utilized to evaluate them.  
Contextual information has also been utilized for process enhancement [4; 5; 18]. For example, a generic framework for operating context-aware performance analysis was presented to analyze performance characteristics from multiple perspectives [5]. In addition, the authors showed that the accuracy of the prediction of process performance indicators, including remaining time, can be improved using contextual data [4]. There was also an approach to utilize process contexts to receive more homogeneous data from trace clustering [18].  
There have been multiple attempts on context-aware process mining to discover realistic processes and improve them. As mentioned in Section 1, however, there was a lack of connecting resource network models to process contexts. In particular, although the quality is a necessary measure as one of the Devil's quadrangles, it has not been a crucial element in contextual data. As such, our work resolves these limitations and presents a quality-aware resource model discovery method.