Authors:
Matthias Kraus
;
Niklas Weiler
;
Thorsten Breitkreutz
;
Daniel A. Keim
and
Manuel Stein
Affiliation:
Data Analysis and Visualization, University of Konstanz and Germany
Keyword(s):
Visualization Theory, Uncertainty, Validation, Visual Analytics.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
Data Management and Knowledge Representation
;
General Data Visualization
;
Information and Scientific Visualization
;
Knowledge-Assisted Visualization
;
Perception and Cognition in Visualization
;
Spatial Data Visualization
;
Uncertainty Visualization
;
Virtual Environments and Data Visualization
;
Visual Analytical Reasoning
;
Visual Data Analysis and Knowledge Discovery
;
Visual Representation and Interaction
;
Visualization Applications
;
Visualization Taxonomies and Models
Abstract:
Visual data exploration is a useful means to extract relevant information from large sets of data. The visual analytics pipeline processes data recorded from the real world to extract knowledge from gathered data. Subsequently, the resulting knowledge is associated with the real world and applied to it. However, the considered data for the analysis is usually only a small fraction of the actual real-world data and lacks above all in context information. It can easily happen that crucial context information is disregarded, leading to false conclusions about the real world. Therefore, conclusions and reasoning based on the analysis of this data pertain to the world represented by the data, and may not be valid for the real world. The purpose of this paper is to raise awareness of this discrepancy between the data world and the real world which has a high impact on the validity of analysis results in the real world. We propose two strategies which help to identify and remove specific di
fferences between the data world and the real world. The usefulness and applicability of our strategies are demonstrated via several use cases.
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