MUSA - a prototype for multiple-step aggregation visualization

It is a common task when analyzing a large dataset (e.g., census database) to create some kind of overview of the original dataset, which is small enough to be easily manipulated, while remains the key characteristics of the data. Many aggregation techniques have been proposed to help users better understand the dataset and find desired information in it. However, the user can easily get lost after several aggregation operations, since there is rarely mechanism facilitating the user to remember what he or she has done in previous steps. In this paper, we present a prototype, namely MUSA, for multiple-step aggregation visualization. We aimed at designing a tool not only to help users obtain various levels of overviews to narrow their selections, but also to effectively visualize the aggregation processes to enhance the context awareness. We also conducted an informal user study to evaluate the tool.