Steerable Self-Driving Data Visualization
Yuyu Luo, Xuedi Qin, Chengliang Chai, Nan Tang, Guoliang Li, Wenbo Li
Abstract
In this work, we present a self-driving data visualization system, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEye</small> , that automatically generates and recommends visualizations based on the idea of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visualization by examples.</i> We propose effective visualization recognition techniques to decide which visualizations are meaningful and visualization ranking techniques to rank the good visualizations. Furthermore, a main challenge of automatic visualization system is that the users may be misled by blindly suggesting visualizations without knowing the user's intent. To this end, we extend <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEye</small> to be easily steerable by allowing the user to use <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">keyword search</i> and providing click-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">faceted navigation</i> . Empirical results, using real-life data and use cases, verify the power of our proposed system.