Accurate Visualization for
Knowledge Discovery in Big-Data Science
Jian Chen
UMDBC
Noon 6 June in 2155
Abstract:
Imagine big computer displays become a space to augment human thinking.
Essential human activities such as seeing, gesturing, and exploring can
couple with powerful computational solutions using natural interfaces
and accurate visualizations. In this talk, I will present research
effort to quantify visualization techniques of all kinds. Our ongoing
work includes research in: (1) perceptually accurate visualization –
constructing a visualization language to study how to depict spatially
complex fields in quantum-physics simulations and brain-imaging
datasets; (2) using space to compensate for limited human memory –
developing new computing and interactive capabilities for bat-flight
motion analysis in a new metaphorical interface; and (3) extending
exploratory metaphors to biological pathways to make possible
integrated analysis of multifaceted datasets. During the talk, I will
point to a number of other projects being carried out by my team. I
will close with some thoughts on automating the evaluation of
visualizations and venture that a science of visualization and
metaphors now has the potential to be developed in full, and that its
success will be crucial in understanding data-to-knowledge techniques
in big data areas.