In recent years, many approaches have been proposed to support business process design, for instance, by providing reference models, retrieving similar business processes or querying process fragments. However, these approaches are still labor-intensive, error-prone and time-consuming. Moreover, they have not yet fully exploited process event logs, which contain useful information about the real execution of business processes. In this paper, we present an innovative approach that extracts information from event logs to develop a useful tool to support the process design. Concretely, we extract the execution order of activities to build a neighborhood context for each activity. We match both activities' labels and their neighborhood contexts to compute the similarity between them. Finally, we propose a query language as a practical tool that allows process designers to query activities and their involved log-based process fragments based on the computed similarity. We developed an application to validate our approach as a proof of concept. We also performed experiments on a large public dataset and experimental results show that our approach is feasible and efficient.