Model-driven visual analytics for big data
Shenghui Cheng, Bing Wang, Wen Zhong, Cong Xie, Salman Mahmood, Jun Wang, Klaus D. Mueller · 2016
The growth of digital data is tremendous. Any aspect of life and matter is being recorded and stored on cheap disks, either in the cloud, in businesses, or in research labs. We can now afford to explore very complex relationships with many variables playing a part. But for this we need powerful tools that allow us to be creative, to sculpt this intricate insight formulated as models from the raw block of data. High-quality visual feedback plays a decisive role here. The subject of this poster is a framework we have developed over the years to make the exploration of large multivariate data more intuitive and direct. The components of this framework were conceived in tight collaborations with domain experts in the fields of climate science, health informatics, computer systems, and others.