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Description: We introduce the concept of Attention-Aware Visualizations (AAVs), which monitor how a user interacts with a visual display over time and use this insight to enhance the visualization. This technique is especially beneficial for pervasive and immersive analytics, allowing for an understanding of which particular visual elements a user focuses on. This knowledge enables visualizations to adapt in real-time to a user's focus, for instance, by accentuating previously unnoticed data. This method is divided into three key areas: (1) measuring where and how a user looks at a visualization; (2) observing the user's focus shifts over a period; and (3) dynamically altering the visual display based on the user's current focus levels. In our work, we demonstrate two distinct AAV implementations: a 2D approach that is indifferent to the type of data and suitable for web applications, utilizing an integrated eye-tracking technology to determine where a user is looking, and a 3D technique that is informed by the data, employing a stencil buffer to monitor how individual components of a visualization are viewed. Both strategies offer mechanisms for gathering data on user attention over time and adjusting visual elements accordingly. Furthermore, we share findings from a qualitative assessment that explores how visual feedback and activation mechanisms can effectively capture and re-visualize user attention.