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 for the selected cases and 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.
Highlights
Business process management (BPM) is concerned with the analysis and redesign of organizational processes [14]
Event sequence data from process-aware information systems are increasingly available as event logs to support BPM
We identify five requirements for an interactive log-delta analysis technique
Summary
Business process management (BPM) is concerned with the analysis and redesign of organizational processes [14]. Event sequence data from process-aware information systems are increasingly available as event logs to support BPM. Various techniques have been developed for extracting actionable insights from such event logs, including automated process discovery, conformance checking, variant analysis and performance analysis. All these techniques are specific process mining techniques [27]. One specific challenge for process mining techniques is the effective distinction between typical and atypical behavior. Process discovery techniques considering the overall event log often produce large spaghetti-like models and models having either low level of fitness to the event log or low precision or generalization [27]. There are largely two approaches to tackle this problem: first, by automatically
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