Visualization for enabling human-in-the-loop in trace clustering-based process mining tasks

Abstract

Process mining encompasses a series of tasks aimed at discovering knowledge about business processes from event logs underlying information systems deployed in organizations. Considering real-world business processes, high-complexity issues often prevent process mining techniques from producing satisfactory results. Business processes’ complexity arises from: (i) high behavioral variability as presented in unstructured processes, e.g. knowledge intensive processes, in which decisions commonly dependent on human actions; (ii) data volume, as it can reach big data levels in organizations with high-volume operations. Trace clustering can support mitigating high-complexity related issues. The process instance profiles resulting from trace clustering divide a complex problem into smaller and simpler ones. However, interpreting clustering results frequently requires decision-making and reasoning that might benefit from domain experts’ knowledge. Especially, in trace clustering-based process mining tasks, domain experts involvement enable results evaluation from the business process perspective. In this paper, a proposal for a trace clustering results visualization is presented. This visualization strategy supports evaluation from a business process perspective, enabling human-in-the-loop strategies. In order to illustrate the usefulness and appropriateness of the visualization, we present three use cases modeled on real-world event logs.