User interaction logging is a powerful tool for user behavior studies, usability testing, and system metrics analysis. It may also be applied in large data contexts, such as social networks analysis, helping data scientists to understand social patterns. Data scientists, User Experience (UX) designers, Human-Computer Interaction (HCI) practitioners, and software engineers have been performing the analysis of this kind of data to obtain knowledge regarding the source system’s usage. User interaction log data, however, can also be critical for final users themselves. They can use interaction log data, for example, (i) to revisit his own interaction path, redoing his steps that lead to a relevant insight or discovery; (ii) to learn from someone else’s interaction path new ways to perform a given task; (iii) or even to analyze critical steps of a process supported by the source system. The need for final users to consume interaction log data is presenting significant challenges for UX researchers. Influenced by Semiotic Engineering, a HCI theory that views human-computer interaction as a form of human communication between designers and users mediated by a computer system, we propose three user interaction log abstraction levels - strategic, tactical, and operational - to frame and guide user interaction logs’ UX design. In this paper, we discuss how those abstraction levels can be used as UX design guidelines and present some research questions to be explored - how source system captures interaction log is central for log analysis strategy and how a strategic level can be identified thought the analysis of interaction logs data from other abstraction levels.