Understanding and improving process analysts’ comprehension of process mining visualizations

Abstract

Process mining (PM) has become fundamental for data-driven process analysis. It enables organizations to identify inefficiencies and areas for improvement. Central to the effectiveness of PM is the use of visualizations and the ability of process analysts to comprehend the visualizations generated by PM algorithms. This doctoral research aims to address the challenge of enhancing the comprehensibility of PM visualizations for process analysts. The study has two key objectives: (1) to establish an improved understanding of how process analysts interpret PM visualizations and (2) to design and evaluate novel visualizations that improve comprehensibility. Through a combination of theoretical frameworks and empirical studies, this research develops an assessment framework to evaluate the comprehensibility of PM visualizations. Additionally, the research proposes novel visualization designs, which are assessed through a series of experiments to determine their impact on analysts' performance. The findings contribute to the field of PM by providing insights that can guide the design of more user-friendly visualizations, which facilitates better decision-making and process improvement within organizations.