The Haunted Swamps of Heuristics: Uncertainty in Problem Solving

Information

• Publication Type: Article in a Book
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: 2014
• Booktitle: Scientific Visualization
• Chapter: Uncertainty, Multifield, Biomedical, and Scalable Visualization
• Editor: Charles D. Hansen, Min Chen, Christopher R. Johnson, Arie E. Kaufman, Hans Hagen
• ISBN: 978-1-4471-6496-8
• Note: Chapter 5
• Publisher: Springer London
• Pages: 51 – 60

Abstract

In scientific visualization the key task of research is the provision of insight into a problem. Finding the solution to a problem may be seen as finding a path through some rugged terrain which contains mountains, chasms, swamps, and few flatlands. This path—an algorithm discovered by the researcher—helps users to easily move around this unknown area. If this way is a wide road paved with stones it will be used for a long time by many travelers. However, a narrow footpath leading through deep forests and deadly swamps will attract only a few adventure seekers. There are many different paths with different levels of comfort, length, and stability, which are uncertain during the research process. Finding a systematic way to deal with this uncertainty can greatly assist the search for a safe path which is in our case the development of a suitable visualization algorithm for a specific problem. In this work we will analyze the sources of uncertainty in heuristically solving visualization problems and will propose directions to handle these uncertainties.

Additional Files and Images

Weblinks

• Entry in the publication database of TU-Wien

BibTeX

@incollection{Groeller_Eduard_2014_THS,
  title =      "The Haunted Swamps of Heuristics: Uncertainty in Problem
               Solving",
  author =     "Artem Amirkhanov and Stefan Bruckner and Christoph Heinzl
               and Eduard Gr\"{o}ller",
  year =       "2014",
  abstract =   "In scientific visualization the key task of research is the
               provision of insight into a problem. Finding the solution to
               a problem may be seen as finding a path through some rugged
               terrain which contains mountains, chasms, swamps, and few
               flatlands. This path—an algorithm discovered by the
               researcher—helps users to easily move around this unknown
               area. If this way is a wide road paved with stones it will
               be used for a long time by many travelers. However, a narrow
               footpath leading through deep forests and deadly swamps will
               attract only a few adventure seekers. There are many
               different paths with different levels of comfort, length,
               and stability, which are uncertain during the research
               process. Finding a systematic way to deal with this
               uncertainty can greatly assist the search for a safe path
               which is in our case the development of a suitable
               visualization algorithm for a specific problem. In this work
               we will analyze the sources of uncertainty in heuristically
               solving visualization problems and will propose directions
               to handle these uncertainties.",
  booktitle =  "Scientific Visualization",
  chapter =    "Uncertainty, Multifield, Biomedical, and Scalable
               Visualization",
  editor =     "Charles D. Hansen, Min Chen, Christopher R. Johnson, Arie E.
               Kaufman, Hans Hagen",
  isbn =       "978-1-4471-6496-8",
  note =       "Chapter 5",
  publisher =  "Springer London",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2014/Groeller_Eduard_2014_THS/",
}