Colloquy Cycle on Friday, November 9, 2018 - 10:30

Friday, November 9, 2018 - 10:30
Seminar room E186 (Favoritenstraße 9, Stiege 1, 5th floor)

Visual Data Science and its role in hypothesis generation and computational steering

Prof. Helwig Hauser (University of Bergen)
45 + 15
Meister Edi Gröller
This talk is a sponsored talk of the Colloquy Cycle

Visualization is embracing the new paradigm of data science, where hypotheses are formulated on the basis of existing data, for example from medical cohort studies or ensemble simulation.  New methods for the interactive visual exploration of rich datasets are supporting data scientists as well as solutions that are based on the integration of automated analysis techniques with interactive visual methods.  In this talk, we discuss interactive visual hypothesis generation and recent related work.  We also look at interactive visual steering, where interactive visual solutions are used to enter an iterative process of modeling.  Furthermore, an attempt of looking into the new future of potentially upcoming visualization research is also included with the hope of spawning an interesting related discussion. 

Biographical Note

Helwig Hauser graduated in 1995 from the Vienna University of Technology (TU Wien) in Austria and finished his PhD project on the visualization of dynamical systems (flow visualization) in 1998.  In 2003, he did his Habilitation at TU Wien, entitled ''Generalizing Focus+Context Visualization''.  After first working for TU Wien as assistant and later as assistant professor (1994–), he changed to the then new VRVis Research Center in 2000 (having been one of the founding team), where he led the basic research group on interactive visualization (until 2003), before he then became the scientific director of VRVis.  Since 2007, he is professor in visualization at the University of Bergen in Norway, where he built up a new research group on visualization, see   

Importance-Driven Exploration of Molecular Dynamics Simulations (DAEV)

Thomas Trautner (Inst. 193-02 CG)
20 + 10
The aim of this thesis is a novel real-time visualization approach for exploring molecular dynamics (MD-)simulations. Through the constantly improving hardware and ever-increasing computing power, MD-simulations are more easily available. Additionally, they consist of hundreds, thousands or even millions of individual simulation frames and are getting more and more detailed. The calculation of such simulations is no longer limited by algorithms or hardware, nevertheless it is still not possible to efficiently explore this
huge amount of simulation data, as animated 3D visualization, with ordinary and well established visualization tools. Using current software tools, the exploration of such long simulations takes too much time and due to the complexity of large molecular scenes, the visualizations highly suffer from visual clutter. It is therefore very likely that the user will miss important events.
Therefore, we designed a focus & context approach for MD-simulations that guides the
user to the most relevant temporal and spatial events, and it is no longer necessary to explore the simulation in a linear fashion. Our contribution can be divided into the
following four topics:
1. Spatial importance through different levels of detail. Depending on the type of research task, different geometrical representations can be selected for both, focus and context elements.
2. Importance driven visibility management through ghosting, to prevent context
elements from occluding focus elements.
3. Temporal importance through adaptive fast-forward. The playback speed of the
simulation is thereby dependent on a single or a combination of multiple importance
4. Visual declutter of accumulated frames through motion blur, which additionally
illustrates the playback speed-up.
Since the very beginning, this work was developed in close cooperation with biochemists from the Loschmidt Laboratories in Brno, Czech Republic. Together, we analyzed different use cases demonstrating the flexibility of our novel focus & context approach