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The analysis of execution traces can be useful in many software engineering activities including debugging, feature enhancement, performance analysis, and any other task that requires some degree of understanding of the way a system behaves. Traces, however, tend to be considerably large, which often hinders effective analysis of their content. There is a need to investigate ways to help software engineers find and understand important information conveyed in a trace despite the trace being massive. Motivated by the work done in the area of text mining, we propose, in this paper, a trace exploration approach based on examining the trace execution phases. The approach consists of automatically identifying relevant information about the phases as well as the ability to provide an efficient representation of the flow of phases by detecting redundant phases using a cosine similarity metric. We applied our approach to large traces generated from two different systems and were able to quickly understand their content and extract higher level views that characterize the essence of the information conveyed in these traces.