Adaptive Visual Computing

Meister Eduard Gröller
Adaptive Visual Computing, 31. August 2017, University of Konstanz

Information

Abstract

Visual computing uses computer-supported, interactive, visual representations of (abstract) data to amplify cognition. In recent years data complexity concerning volume, veracity, velocity, and variety has increased considerably. Several adaptive visual computing approaches are discussed in detail. Data-sensitive navigation for user-interface elements is presented. The approach normalizes user input according to visual change, and also visually communicates this normalization. In this way, output-sensitive interactions can be realized. Quantitative and reproducible linking & brushing as integral part of visual analytics is approached through structured brushing, percentile brushes, linked statistics, and change visualization. Multiscale models, e.g., from structural biology, require multiscale dynamic color mapping with sometimes overlapping or contradicting colors. We present a technique, which adaptively, based on the current scale level, nonlinearly and seamlessly adjusts the color scheme to depict or distinguish the currently best visible structural information. Adaptive visual computing is addressing the amplified data complexity through increased scalability. Research challenges and directions are sketched at the end of the talk.

Additional Files and Images

No additional files or images.

Weblinks

No further information available.

BibTeX

@talk{Groeller-2017-AVC,
  title =      "Adaptive Visual Computing",
  author =     "Meister Eduard Gr{"o}ller",
  year =       "2017",
  abstract =   "Visual computing uses computer-supported, interactive,
               visual representations of (abstract) data to amplify
               cognition. In recent years data complexity concerning
               volume, veracity, velocity, and variety has increased
               considerably. Several adaptive visual computing approaches
               are discussed in detail. Data-sensitive navigation for
               user-interface elements is presented. The approach
               normalizes user input according to visual change, and also
               visually communicates this normalization. In this way,
               output-sensitive interactions can be realized. Quantitative
               and reproducible linking & brushing as integral part of
               visual analytics is approached through structured brushing,
               percentile brushes, linked statistics, and change
               visualization. Multiscale models, e.g., from structural
               biology, require multiscale dynamic color mapping with
               sometimes overlapping or contradicting colors. We present a
               technique, which adaptively, based on the current scale
               level, nonlinearly and seamlessly adjusts the color scheme
               to depict or distinguish the currently best visible
               structural information. Adaptive visual computing is
               addressing the amplified data complexity through increased
               scalability. Research challenges and directions are sketched
               at the end of the talk. ",
  month =      aug,
  event =      "Visit of University of Konstanz",
  location =   "University of Konstanz",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2017/Groeller-2017-AVC/",
}