<p>From the time an aircraft arrives at an airport until its departure, a set of planned services, such as fuelling, maintenance, and exchange of passengers, called Ground Handling Operations (GHOs), are executed to prepare the aircraft for the next journey. These services must be completed as scheduled. Otherwise, significant delays may happen, increasing the costs for airport stakeholders. Attempts have been made to analyze GHOs using mathematical modeling and simulations. However, such models typically use external, highly sensitive information that, in practice, is not shared by airline companies. This paper aims to fill this gap through a visual analytics system called Sequential Rule Visualization (SeRViz), which combines matrix-like visual representations with Sequential Rule Mining (SRM) to support the exploratory analysis of airport operations using only GHO logs and publicly available data (e.g., weather). In contrast with typical approaches that visualize raw sequence data to aid in discovering patterns, SeRViz focuses on visualizing patterns that have already been mined—specifically, sequential rules that represent frequent temporal behaviors in GHO logs. SeRViz was designed to meet requirements set by airport experts. Through tests and interviews, the combination of visualization and SRM was shown to be promising in enhancing experts’ analytical power without increasing the cognitive overload imposed on users in analyzing complex sequential information.</p>