Data Reduction Techniques for Simulation, Visualization and Data Analysis
| dc.contributor.author | Li, S. | en_US |
| dc.contributor.author | Marsaglia, N. | en_US |
| dc.contributor.author | Garth, C. | en_US |
| dc.contributor.author | Woodring, J. | en_US |
| dc.contributor.author | Clyne, J. | en_US |
| dc.contributor.author | Childs, H. | en_US |
| dc.contributor.editor | Chen, Min and Benes, Bedrich | en_US |
| dc.date.accessioned | 2018-09-19T15:32:53Z | |
| dc.date.available | 2018-09-19T15:32:53Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs. | en_US |
| dc.description.number | 6 | |
| dc.description.sectionheaders | Articles | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 37 | |
| dc.identifier.doi | 10.1111/cgf.13336 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 422-447 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13336 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13336 | |
| dc.publisher | © 2018 The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | data reduction techniques | |
| dc.subject | simulation, data analysis | |
| dc.subject | survey | |
| dc.subject | General and reference → Document types → Surveys and overviews, Information systems → Data management systems → Data structures → Data layout → Data compression | |
| dc.title | Data Reduction Techniques for Simulation, Visualization and Data Analysis | en_US |