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Process workers may vary the normal execution of a business process to adjust to changes in their operational environment, e.g., changes in workload, season, or regulations. Changes may be simple, such as skipping an individual activity, or complex, such as replacing an entire procedure with another. Over time, these changes may negatively affect process performance; hence, it is important to identify and understand them early on. As such, a number of techniques have been developed to detect process drifts, i.e., statistically significant changes in process behavior, from process event logs (offline) or event streams (online). However, detecting a drift without characterizing it, i.e., without providing explanations on its nature, is not enough to help analysts understand and rectify root causes for process performance issues. Existing approaches for drift characterization are limited to simple changes that affect …