# Quality-Aware Resource Model Discovery
## 6 Conclusions  
This paper suggested a method to derive a quality-aware resource model. In this regard, we employed a model discovery algorithm, i.e., transition system, and a series of statistical approaches. In addition, we provided tooling support for the proposed approach, and the manufacturing case study was presented to validate our approach. As a result, we identified that our approach is effective in understanding the relationship between resource paths and quality. This study has the strength of employing transition systems, i.e., one of low-level process models, which perfectly fit the event log and so we do not need to validate the model from the behavior perspective. Transition systems define states and transitions from event logs as they are, instead of discovering control-flows, such as OR-split/join and AND-split/join. Thus, the model holds all of the behavior in event logs. In addition, our approach allows model simplification. However, this is a process of integrating and replacing the resources into a dummy that does not affect the quality, rather than a filtering process that removes specific resources from the model. Thus, we can determine that the model fits the log by comparing the simplified model and the traces replaced by dummy resources. Regarding the quality perspective as well as the behavior, we need to consider a couple of viewpoints for the validation: (i) whether the process instances from the model are well predicted as high or low quality and (ii) whether the resources engaged in each activity are well-segmented as the high- or low-quality group. To validate the first viewpoint, we can employ the general validation method for supervised learning classification tasks, i.e., dividing the event log into training and validation datasets and evaluating the model by comparing the actual and the predicted results with a confusion matrix. As far as the second viewpoint is concerned, in the process of the statistical analyses on resources, we already use the \(p\)-value to support the significance of the results. That is, we can argue that the model performs the validation itself. In addition to this, we plan to build a systematic validation method to improve the completeness of the proposed approach through future work. This work has several limitations. First, our approach is relatively automated, but it still requires user intervention, e.g., user-specified weights. In other words, our approach partly depends on practitioners in a specific domain for better modeling and visualization. Therefore, future research should deal with how to derive the optimal parameters for a particular process. In addition, our approach effectively understands the relationship between resource paths and quality, but it does not hold the ability to predict the quality for a specific case. We need to develop a method to predict the quality value based on resource paths and other attributes. Furthermore, we should extend our approach to an online-fashioned method based on real-time data that estimates quality and recommends the optimized resource paths at the moment the process is running. As future works, we will conduct research to construct a model that reflects the states of the resources based on the other data, e.g., sensor data, and that have a relationship with quality. Furthermore, we will develop a method to present our approach in an online setting and predict the quality of cases. In addition, we have a plan to conduct more case studies with other domains, e.g., healthcare and service sectors. Methodology, software, and formal analysis, All authors; writing--original draft preparation, M.C. and G.P. ; writing--review and editing, M.S., J.L. and E.K. ; supervision, M.S. All authors have read and agreed to the published version of the manuscript.