Christoph Heinzl, Stefan Bruckner, Meister Eduard Gröller

**Guest editorial—Uncertainty and parameter space analysis in visualization**

*Computer & Graphics*, 41:A1-A2, June 2014.

- Publication Type: Journal Paper (without talk)
- Workgroup(s)/Project(s):
- Date: June 2014
- Journal: Computer & Graphics
- Pages (from): A1
- Pages (to): A2
- Volume: 41

Providing just insight into available data of a problem seems not to be sufficient anymore: Uncertainty and parameter space analyses in visualization are becoming more prevalent and may be found in astronomic, (bio)-medical, industrial, and engineering applications. The major goal is to find out, at which stage of the pipeline - from data acquisition to the final rendering of the output image - how much uncertainty is introduced and consequently how the desired result (e.g., a dimensional measurement feature) is affected. Therefore effective methods and techniques are required by domain specialists, which help to understand how data is generated, how reliable is the generated data, and where and why data is uncertain.

Furthermore, as the problems to investigate are becoming increasingly complex, also finding suitable algorithms providing the desired solution tends to be more difficult. Additional questions may arise, e.g., how does a slight parameter change modify the result, how stable is a parameter, in which range is a parameter stable or which parameter set is optimal for a specific problem. Metaphorically speaking, an algorithm for solving a problem may be seen as finding a path through some rugged terrain (the core problem) ranging from the high grounds of theory to the haunted swamps of heuristics. There are many different paths through this terrain with different levels of comfort, length, and stability. Finding all possible paths corresponds in our case to doing an analysis of all possible parameters of a problem solving algorithm, which yields a typically multi-dimensional parameter space. This parameter space allows for an analysis of the quality and stability of a specific parameter set. In many cases of conventional visualization approaches the issues of uncertainty and parameter space analyses are neglected. For a long time, uncertainty - if visualized at all - used to be depicted as blurred data. But in most cases the uncertainty in the base data is not considered at all and just the quantities of interest are calculated. And even to calculate these quantities of interest, too often an empirically found parameter set is used to parameterize the underlying algorithms without exploring its sensitivity to changes and without exploring the whole parameter space to find the global or a local optimum.

This tutorial aims to open minds and to look at our data and the parameter sets of our algorithms with a healthy skepticism. In the tutorial we combine uncertainty visualization and parameter space analyses which we believe is essential for the acceptance and applicability of future algorithms and techniques. The tutorial provides six sessions starting with an overview of uncertainty visualization including a historical perspective, uncertainty modeling and statistical visualization. The second part of the tutorial will be dedicated to structural uncertainty, parameter space analysis, industrial applications of uncertainty visualization and an outlook in this domain.

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@article{Groeller_2014_UPS, title = "Guest editorial—Uncertainty and parameter space analysis in visualization", author = "Christoph Heinzl and Stefan Bruckner and Meister Eduard Gr\"{o}ller", year = "2014", abstract = "Within the past decades visualization advanced to a powerful means of exploring and analyzing data. Recent developments in both hard- and software contributed to previously unthinkable evaluations and visualizations of data with strongly increasing sizes and levels of complexity. Providing just insight into available data of a problem seems not to be sufficient anymore: Uncertainty and parameter space analyses in visualization are becoming more prevalent and may be found in astronomic, (bio)-medical, industrial, and engineering applications. The major goal is to find out, at which stage of the pipeline - from data acquisition to the final rendering of the output image - how much uncertainty is introduced and consequently how the desired result (e.g., a dimensional measurement feature) is affected. Therefore effective methods and techniques are required by domain specialists, which help to understand how data is generated, how reliable is the generated data, and where and why data is uncertain. Furthermore, as the problems to investigate are becoming increasingly complex, also finding suitable algorithms providing the desired solution tends to be more difficult. Additional questions may arise, e.g., how does a slight parameter change modify the result, how stable is a parameter, in which range is a parameter stable or which parameter set is optimal for a specific problem. Metaphorically speaking, an algorithm for solving a problem may be seen as finding a path through some rugged terrain (the core problem) ranging from the high grounds of theory to the haunted swamps of heuristics. There are many different paths through this terrain with different levels of comfort, length, and stability. Finding all possible paths corresponds in our case to doing an analysis of all possible parameters of a problem solving algorithm, which yields a typically multi-dimensional parameter space. This parameter space allows for an analysis of the quality and stability of a specific parameter set. In many cases of conventional visualization approaches the issues of uncertainty and parameter space analyses are neglected. For a long time, uncertainty - if visualized at all - used to be depicted as blurred data. But in most cases the uncertainty in the base data is not considered at all and just the quantities of interest are calculated. And even to calculate these quantities of interest, too often an empirically found parameter set is used to parameterize the underlying algorithms without exploring its sensitivity to changes and without exploring the whole parameter space to find the global or a local optimum. This tutorial aims to open minds and to look at our data and the parameter sets of our algorithms with a healthy skepticism. In the tutorial we combine uncertainty visualization and parameter space analyses which we believe is essential for the acceptance and applicability of future algorithms and techniques. The tutorial provides six sessions starting with an overview of uncertainty visualization including a historical perspective, uncertainty modeling and statistical visualization. The second part of the tutorial will be dedicated to structural uncertainty, parameter space analysis, industrial applications of uncertainty visualization and an outlook in this domain. ", month = jun, journal = "Computer & Graphics", volume = "41", pages = "A1--A2", URL = "/research/publications/2014/Groeller_2014_UPS/", }

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