@article{PB-VRVis-2016-011,
author = {Radoš, Sanjin and Splechtna, Rainer and Matkovi{\'c}, Kresimir and Duras, Mario and Gr{\"o}ller, Eduard and Hauser, Helwig},
title = {Towards Quantitative Visual Analytics with Structured Brushing and Linked
  Statistics},
year = {2016},
journaltitle = {Computer Graphics Forum},
url = {https://www.vrvis.at/publications/PB-VRVis-2016-011},
issn = {1467-8659},
pages = {1-1},
volume = {35},
abstract = {Until now a lot of visual analytics predominantly delivers qualitative  results based, for example,on ac ontinuous color map or a detailed  spatial encoding. Important target applications, however, such as medical  diagnosis and decision making, clearly benefit from quantitative analysis  results. In this paper we propose several specific extensions to the  well-established concept of linking and brushing in order to make the  analysis results more quantitative. We structure the brushing space in  order to improve the reproducibility of the brushing operation, e.g.,by  introducing the percentile grid.We also enhance the linked visualization  with overlaid descriptive statistics to enable a more quantitative reading  of the resulting focus+context visualization. Additionally, we introduce  two novel brushing techniques: the percentile brush and the Mahalanobis  brush. Both use the underlying data to support statistically meaningful  interactions with the data. We illustrate the use of the new techniques in  the context of two case studies, one based on meteorological data and the  other one focused on data from the automotive industry where we evaluate a  shaft design in the context of mechanical power transmission in cars.},
number = {3},
}