III: Small: RUI: Collaborative Research: ANTE - A Four-Tier Framework to Boost Visual Literacy for High Dimensional Data

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This is a collaborative research project involving SUNY Stony Brook and Union College, an undergraduate institution. With the massive availability of data, the need to understand and be comfortable with data has gained increasing importance. There is now a great demand for individuals that have the skills to extract meaning from data. Academic programs in data science been created in many institutions, but going back to school to formally study this topic is not possible for a large segment of the population. In addition, not everyone really needs to become a formal data scientist to be competitive in this increasingly data-centric society and workplace where it can be of great benefit to become more data literate. Visualizations, such as the bar charts, line plots, maps, etc. that most people are familiar with, are helpful in explaining data. However, today's data sets often combine many different kinds of information and are, therefore, too complex to be represented with these basic visualizations. The goal of this project is to develop a visualization system that can represent data in such a way that a user can make sense of complex data without extensive training. This will involve advances in visualization techniques as well as novel approaches to presenting visualizations in an engaging way. The ANTE (Appeal, Narrate, Transform, Engage) system developed in this project has good potential to help increase the ability of citizens to become more knowledgeable participants in an increasingly data-centric society. The project provides research training for graduate students at SUNY Stony Brook and undergraduate students at Union College. The visualization tools and games will make an excellent environment for teaching both data and visual literacy, at all education levels. The ANTE framework seeks to achieve its goal by developing novel solutions that address these four complementing elements: Appeal, Narrate, Transform, Engage. ANTE will appeal to the user's existing visual literacy by defining new powerful techniques that can faithfully transform complex high-dimensional data into simpler representations. One such representation is a novel 2D contextual data map that unlike other maps of this kind can maintain all relationships in the data matrix -- data to data, data to attribute, and attribute to attribute. Another is an interactive 3D shaded display that replaces the complex scatterplot matrices that are in standard use for high-D visualization. ANTE will use natural language to narrate the visualizations it produces. ANTE will also use animations to show how different representations of the data set can be transformed into each other. The project will investigate the use of such animations for teaching users to interpret more advanced visualizations. Finally, ANTE entices user engagement into data by offering (1) support for story authoring by ways of visual causality analysis; (2) capabilities for designing compelling infographics by fusing data with contextual images retrieved with web-scale image databases; (3) a narrative interface that uses learning from analogy to teach users more complex visualizations from familiar ones; and (4) a framework that employs techniques gleaned from gamification to incentivize engagement in user evaluation studies for all of our proposed techniques. Further information, research paper and developed artifacts, such as web interfaces to the visualization systems, data, video links, etc. are available at the project web site (http://www3.cs.stonybrook.edu/~mueller/ANTE/).