- Walking Wales: the visualisation challenge
- Word Clouds: Can they be misleading?
- VIPS-Visual Interactive Parameter Steering
- Confirmation bias : do multiple views really help?
- Visual Query Interface for Infrastructure Networks
- MADucator – Matrix Educator
- Concensus Matrix Sort
- Multiscale Multiples: An Overview+Detail Interface for Small Multiples
- Analyzing Image Similarity to Detect Disguised Plagiarism
- Tackling the Multi-Analyst Problem in Soccer: How to Improve Collaborative Pattern Detection in Team Sports
- MDSQ – Quality Assessment of Distance-Preserving Projections
- Internal Quality Measures for Subspace Clusterings
- Exploration of Datasets for Visualizations of High-Dimensional Data
- Search and Visual Exploration of Scientific Literature
- Star Glyph – Optimal Dimension Layout
- Visual Analytics of Molecular Neurochemicals and Biological Markers in Mental Illness
- Ensembles of classifiers: Visual construction of classification models
- Cutting-Down the Complexity of Parallel Coordinates Plots in High-Dimensional Data
- Investigating Analytic Behavior in VA
- Hierarchical Matrix Visualization
- Realisierung und Evaluierung einer stereoskopischen 3D Perspective Wall Umgebung für verlinkte Informationsvisualisierung (Master)
- Combination of Matrices + Graphs
- TreeMap Evaluation
- Visual Analysis of Language Change over Time
- Visual Parameter Space Analysis of Topic Models
|Theoretical (Analytical):||(3 / 5)|
|Practical (Implementation):||(5 / 5)|
|Literature Work:||(3 / 5)|
Visual analytics combines human thinking and machine capabilities in order to gain new knowledge. Analysts perform nomerous interactions to explore and navigate their datasets, investigate their assumptions, and refine hypothesis. This project will capture, visualize and analyze interaction data with the goal to understand analytic behavior further and to develop methods that support the analyst.
We know little about human processes and interactive behavior in Visual Analytics. Therefore, a (simple) visual analytics system has to be adapted with interaction capturing capabilities. In a second step data has to be captured and analyzed. Further, we aim to derive the analysts focus/interest from analytic provenance information.
- Engage with knowledge generation in visual analytics
- Build on top of an exiting approach to capture and analyze interaction logs
- Conduct a user study to capture provenance data (e.g., with crime analysts)
- Derive interaction measures that can be leveraged to support individual analysts adaptively according to their needs.
- Provide visualizations/statistics for post analytical activities (e.g., “review and verify what has been done”).
- Basic knowledge about information visualization and visual analytics
- Strong conceptual skills (software architectures)
- Strong programmming skills in java and/or web
D, Sacha, A. Stoffel, F. Stoffel, BC Kwon, G. Ellis, D. A. Keim, “Knowledge Generation Model for Visual Analytics”, IEEE Transactions on Visualization and Computer Graphics (Proceedings Visual Analytics Science and Technology 2014), 20(12):1604 – 1613 , DOI: 10.1109/TVCG.2014.2346481, 2014.
D. Sacha, H. Senaratne, BC Kwon, G. Ellis, D. A. Keim: The Role of Uncertainty, Awareness, and Trust in Visual Analytics. IEEE Trans. Vis. Comput. Graph. 22(1): 240-249, 2016.
D. Sacha, I. Boesecke, J. Fuchs and D. A. Keim: Analytic Behavior and Trust Building in Visual Analytics. Accepted at EuroVis Short Paper Track, 2016.