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Informatics 2017, 4(3), 26; doi:10.3390/informatics4030026

Multidimensional Data Exploration by Explicitly Controlled Animation

1
Institute Johann Bernoulli, University of Groningen, 9727 Groningen, The Netherlands
2
ENAC/DEVI, University of Toulouse, 31055 Toulouse, France
3
Institute for Computer Science, University of Rostock, 18051 Rostock, Germany
4
Institute Johann Bernoulli, University of Groningen, 9727 Groningen, The Netherlands
5
ENAC/DEVI, University of Toulouse, 31055 Toulouse, France
*
Author to whom correspondence should be addressed.
Academic Editors: Achim Ebert and Gunther H. Weber
Received: 30 June 2017 / Revised: 1 August 2017 / Accepted: 14 August 2017 / Published: 20 August 2017
(This article belongs to the Special Issue Scalable Interactive Visualization)
View Full-Text   |   Download PDF [6498 KB, uploaded 20 August 2017]   |  

Abstract

Understanding large multidimensional datasets is one of the most challenging problems in visual data exploration. One key challenge that increases the size of the exploration space is the number of views that one can generate from a single dataset, based on the use of multiple parameter values and exploration paths. Often, no such single view contains all needed insights. The question thus arises of how we can efficiently combine insights from multiple views of a dataset. We propose a set of techniques that considerably reduce the exploration effort for such situations, based on the explicit depiction of the view space, using a small multiple metaphor. We leverage this view space by offering interactive techniques that enable users to explicitly create, visualize, and follow their exploration path. This way, partial insights obtained from each view can be efficiently and effectively combined. We demonstrate our approach by applications using real-world datasets from air traffic control, software maintenance, and machine learning. View Full-Text
Keywords: information visualization; small multiple; big data; animation information visualization; small multiple; big data; animation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kruiger, J.F.; Hassoumi, A.; Schulz, H.-J.; Telea, A.; Hurter, C. Multidimensional Data Exploration by Explicitly Controlled Animation. Informatics 2017, 4, 26.

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