Abstract Resource analysis in process mining focuses on understanding the behavior and performance of resources involved in business processes. While previous research has provided various insights into resource-related aspects, the visualization of these insights remains insufficiently developed. This paper addresses this gap by proposing a novel resource analytics technique that integrates metrics from four critical resource-related areas: resource allocation, resource performance, workload distribution, and capacity utilization. The technique incorporates interactive visualizations and process model views to support process analysts in performing resource analysis tasks such as identifying bottlenecks and inefficiencies. Results from a user evaluation demonstrate that the proposed technique enhances the accuracy of resource analysis tasks and is highly regarded for its ease of use and perceived usefulness.

Process mining is a research area focusing on the design of algorithms that can automatically provide insights into business processes. Among the most popular algorithms are those for automated process discovery, which have the ultimate goal to generate a process model that summarizes the behavior recorded in an event log. Past research had the aim to improve process discovery algorithms irrespective of the characteristics of the input log. In this paper, we take a step back and investigate the connection between measures capturing characteristics of the input event log and the quality of the discovered process models. To this end, we review the state-of-the-art process complexity measures, propose a new process complexity measure based on graph entropy, and analyze this set of complexity measures on an extensive collection of event logs and corresponding automatically discovered process models. Our analysis shows that many process complexity measures correlate with the quality of the discovered process models, demonstrating the potential of using complexity measures as predictors of process model quality. This finding is important for process mining research, as it highlights that not only algorithms, but also connections between input data and output quality should be studied.

Abstract Process mining is a family of analytical techniques that extract insights from an event log and present them to an analyst. A key analysis task is to understand the distinctive features of different variants of the process and their impact on process performance. Techniques for log-delta analysis (or variant analysis) put a strong emphasis on automatically extracting explanations for differences between variants. A weakness of them is, however, their limited support for interactively exploring the dividing line between typical and atypical behavior. In this paper, we address this research gap by developing and evaluating an interactive technique for log-delta analysis, which we call InterLog . This technique is developed based on the idea that the analyst can interactively define filter ranges and that these filters are used to partition the log L into sub-logs $$L_1$$ L 1 for the selected cases and $$L_2$$ L 2 for the deselected cases. In this way, the analyst can step-by-step explore the log and manually separate the typical behavior from the atypical. We prototypically implement InterLog and demonstrate its application for a real-world event log. Furthermore, we evaluate it in a preliminary design study with process mining experts for usefulness and ease of use.