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Informatics 2017, 4(3), 27; doi:10.3390/informatics4030027

Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks

Visualization Research Institute, University of Stuttgart; 70569 Stuttgart, Germany
Academic Editors: Achim Ebert and Gunther H. Weber
Received: 1 June 2017 / Revised: 17 August 2017 / Accepted: 18 August 2017 / Published: 26 August 2017
(This article belongs to the Special Issue Scalable Interactive Visualization)
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Abstract

Increasingly fast computing systems for simulations and high-accuracy measurement techniques drive the generation of time-dependent volumetric data sets with high resolution in both time and space. To gain insights from this spatio-temporal data, the computation and direct visualization of pairwise distances between time steps not only supports interactive user exploration, but also drives automatic analysis techniques like the generation of a meaningful static overview visualization, the identification of rare events, or the visual analysis of recurrent processes. However, the computation of pairwise differences between all time steps is prohibitively expensive for large-scale data not only due to the significant cost of computing expressive distance between high-resolution spatial data, but in particular owing to the large number of distance computations ( O ( | T | 2 ) ) , with | T | being the number of time steps). Addressing this issue, we present and evaluate different strategies for the progressive computation of similarity information in a time series, as well as an approach for estimating distance information that has not been determined so far. In particular, we investigate and analyze the utility of using neural networks for estimating pairwise distances. On this basis, our approach automatically determines the sampling strategy yielding the best result in combination with trained networks for estimation. We evaluate our approach with a variety of time-dependent 2D and 3D data from simulations and measurements as well as artificially generated data, and compare it against an alternative technique. Finally, we discuss prospects and limitations, and discuss different directions for improvement in future work. View Full-Text
Keywords: time-dependent data; neural networks; adaptive sampling; volume visualization time-dependent data; neural networks; adaptive sampling; volume visualization
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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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Frey, S. Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks. Informatics 2017, 4, 27.

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