Encoding resource experience for predictive process monitoring

Events recorded during the execution of a business process can be used to train models to predict, at run-time, the outcome of each execution of the process (a.k.a. case). In this setting, the outcome of a case may refer to whether a given case led to a customer complaint or not, or to a product return or other claims, or whether a case was completed on time or not. Existing approaches to train such predictive models do not take into account information about the prior experience of the (human) resources assigned to each task in the process. Instead, these approaches simply encode the resource who performs each task as a categorical (possibly one-hot encoded) feature. Yet, the experience of the resources involved in the execution of a case may clearly have an impact on the case outcome. For example, specialized resources or resources who are familiar with a given type of case, are more likely to execute the tasks in a case faster and more effectively, leading to a higher probability of a positive outcome. Motivated by this observation, this article proposes and evaluates a framework to extract features from event logs that capture the experience of the resources involved in a business process. The framework exploits traditional principles from the literature to capture resource experience, such as experiential learning and social ties on the workplace. The proposed framework is evaluated by comparing the performance of state-of-the-art predictive models trained with and without the proposed resource experience features, using publicly available event logs. The results show that the proposed resource experience features may improve the accuracy of predictive models, but that depends on the process execution context, such as the type of process generating an event log or the type of label that is predicted.

Automated summary

This study proposes a framework to extract experience features of resources from event logs, inspired by principles of experiential learning and social ties in the workplace. Experimental results indicate that the proposed resource experience features may improve the accuracy of predictive models, but the extent of improvement varies depending on the context of process execution.