Scalable Visual Analytics
Goal of this research project is to develop and evaluate a fundamentally new approach to exhaustively search for, and interactively characterize any non-random mutual relationship between attribute dimensions in general data sets. To be able to systematically consider all possible attribute combinations, we propose to apply image analysis to visualization results in order to automatically pre-select only those attribute combinations featuring non-random relationships. To characterize the found information and to build mathematical descriptions, we rely on interactive visual inspection and visualization-assisted interactive information modeling. This way, we intend to discover and explicitly characterize all information implicitly represented in unbiased sets of multi-dimensional data points.
SPP 1335 - Scalable Visual Analytics
12.07.2011 Our paper on Synthetic Generation of High-dimensional Datasets has been accepted at InfoVis 2011.
11.07.2011 Our paper on Perception-based Visual Quality Measures has been accepted at the VAST 2011.
18.11.2009 Our paper on Combining automated analysis and visualization techniques for effective exploration of high-dimensional data has won the SPP Collaboration Award in the DFG priority program on Scalable Visual Analytics (SPP 1335).
08.12.2008 SSP Kick-off Meeting - Dagstuhl
Learning a Perceptual Quality Metric for Correlation in Scatterplots
in Proc. Vision, Modeling and Visualization (VMV), October 2019.
Gaze Visualization for Immersive Video
in Burch, Michael and Chuang, Lewis and Fisher, Brian and Schmidt, Albrecht and Weiskopf, Daniel (Eds.): Eye Tracking and Visualization, Springer, ISBN 978-3319470238, pp. 57-71, March 2017.
Visual Analytics for Development and Evaluation of Order Selection Criteria for Autoregressive Processes
in IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 22, no. 1, pp. 151-159, January 2016.
IEEE VIS 2015 paper
Hierarchical Brushing of High-Dimensional Data Sets Using Quality Metrics
in Proc. Vision, Modeling and Visualization (VMV), pp. 1-8, October 2014.
Visual Analysis of High-Dimensional Spaces
Monsenstein und Vannerdat, ISBN 978-3-95645-286-4, August 2014.
Selecting Coherent and Relevant Plots in Large Scatterplot Matrices
in Computer Graphics Forum, vol. 31, no. 6, pp. 1895-1908, April 2012.
Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data
in IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 17, no. 5, pp. 584-597, February 2011.
Improving the Visual Analysis of High-dimensional Datasets Using Quality Measures
in Proc. IEEE Symposium on Visual Analytics Science and Technology (VAST), pp. 19-26, October 2010.
Visualisierung und Analyse multidimensionaler Datensätze
in Informatik-Spektrum, vol. 33, no. 5, pp. 589-600, September 2010.
Combining automated analysis and visualization techniques for effective exploration of high-dimensional data
in Proc. IEEE Symposium on Visual Analytics Science and Technology (VAST), pp. 59-66, October 2009.
Won the SPP Collaboration Award in the DFG priority program on Scalable Visual Analytics (SPP 1335).