Efficient Adaptive Multiresolution Aggregations of Spatio-temporal Ensembles
| dc.contributor.author | Borrelli, Gabriel | en_US |
| dc.contributor.author | Evers, Marina | en_US |
| dc.contributor.author | Linsen, Lars | en_US |
| dc.contributor.editor | Reina, Guido | en_US |
| dc.contributor.editor | Rizzi, Silvio | en_US |
| dc.date.accessioned | 2024-05-21T08:24:08Z | |
| dc.date.available | 2024-05-21T08:24:08Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Spatio-temporal ensemble data consist of several simulation runs with multiple spatial and a temporal dimension, where the runs are obtained using different parameter settings or initial conditions for the simulation. During analysis, one is interested in investigating the different facets of space, time, and parameter values. When globally analyzing some facet(s), others shall be aggregated to generate summary visualizations. Due to the large amount of data that an ensemble consists of, one may want to generate summary visualizations at multiple levels of detail. Wavelet transforms are a well-known concept for efficiently switching between multiple resolutions. We propose to extend this concept to ensemble data, where individual facets may be aggregated adaptively. We present how to apply the scheme for any data sizes to generate correct averages even when the number of samples is not a power of two in each dimension. We further develop an out-of-core strategy to handle large data sizes. Our scheme is coupled with common 1D, 2D, and 3D visualization methods for an interactive visual analysis of the ensemble data. | en_US |
| dc.description.sectionheaders | Papers | |
| dc.description.seriesinformation | Eurographics Symposium on Parallel Graphics and Visualization | |
| dc.identifier.doi | 10.2312/pgv.20241128 | |
| dc.identifier.isbn | 978-3-03868-243-1 | |
| dc.identifier.issn | 1727-348X | |
| dc.identifier.pages | 11 pages | |
| dc.identifier.uri | https://doi.org/10.2312/pgv.20241128 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/pgv20241128 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → Parallel algorithms; Human-centered computing → Scientific visualization | |
| dc.subject | Computing methodologies → Parallel algorithms | |
| dc.subject | Human centered computing → Scientific visualization | |
| dc.title | Efficient Adaptive Multiresolution Aggregations of Spatio-temporal Ensembles | en_US |