Trace analysis techniques are used by software engineers to understand the behaviour of large systems. This understanding can facilitate various software maintenance activities including debugging and feature enhancement. However, traces usually tend to be very large, which makes it difficult for software engineers to unveil the key logic and functionalities embedded in a program's execution. Hence, it is necessary to develop methods and tools that can efficiently identify the important information contained in a large trace. In this paper, we propose an approach that builds on the concept of trace segmentation to extract the major components of a traced scenario. Our approach is based on Gestalt theory and the Helmholtz principle. We show the effectiveness of our approach by applying it to a dataset of large traces.