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The process of scientific visualization is inherently iterative. A good visualization comes from experimenting with visualization, rendering, and viewing parameters to bring out the most relevant information in the data. A good data visualization system thus lets scientists interactively explore the parameter space intuitively. The more efficient the system, the fewer the number of iterations needed for parameter selection. Over the past 10 years, significant efforts have gone into advancing visualization technology (such as real-time volume rendering and immersive environments), but little into coherently representing the process and results (images and insights) of visualization. This information about the data exploration should be shared and reused. In particular, for types of data visualization with a high cost of producing images and less than obvious relationship between the rendering parameters and the image produced, a visual representation of the exploration process can make the process more efficient and effective. This visual representation of data exploration process and results can be incorporated into and become a part of the user interface of a data exploration system. That is, we need to go beyond the traditional graphical user interface (GUI) design by coupling it with a mechanism that helps users keep track of their visualization experience, use it to generate new visualizations, and share it with others. Doing so can reduce the cost of visualization, particularly for routine analysis of large-scale data sets