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Breaking the Curse of Visual Data Exploration: Improving Analyses by Building Bridges between Data World and Real World

Topics: Data Management and Knowledge Representation; Information and Scientific Visualization; Knowledge-Assisted Visualization; Perception and Cognition in 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

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. (More)

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Paper citation in several formats:
Kraus, M.; Weiler, N.; Breitkreutz, T.; Keim, D. and Stein, M. (2019). Breaking the Curse of Visual Data Exploration: Improving Analyses by Building Bridges between Data World and Real World. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - IVAPP; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 19-27. DOI: 10.5220/0007257400190027

@conference{ivapp19,
author={Matthias Kraus. and Niklas Weiler. and Thorsten Breitkreutz. and Daniel A. Keim. and Manuel Stein.},
title={Breaking the Curse of Visual Data Exploration: Improving Analyses by Building Bridges between Data World and Real World},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - IVAPP},
year={2019},
pages={19-27},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007257400190027},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - IVAPP
TI - Breaking the Curse of Visual Data Exploration: Improving Analyses by Building Bridges between Data World and Real World
SN - 978-989-758-354-4
IS - 2184-4321
AU - Kraus, M.
AU - Weiler, N.
AU - Breitkreutz, T.
AU - Keim, D.
AU - Stein, M.
PY - 2019
SP - 19
EP - 27
DO - 10.5220/0007257400190027
PB - SciTePress