Towards Perception-aware Interactive Data Visualization Systems

An often overlooked element of the interactive data visualization stack is the human in the loop. While computational and data processing capabilities have increased over time, human limits have remained constant. In this light, we describe extensions to client-server database-driven visualization systems that are both customized to interactive workloads, and support perceptual models that approximate the human’s ability to decode visually encoded information. We recognize and accommodate human perceptual limitations as a way to minimize computation, network and rendering costs, and support high frame-rate interactions. Based on these models, we propose to answer a critical question: how can these models inform approximation decisions that improve end-to-end visualization performance? In this short paper, we describe research efforts towards using these limits to automatically approximate data transformations that are perceptually indistinguishable while applying database optimization techniques to minimize latency.