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Data Reduction Techniques for Simulation, Visualization and Data Analysis
Author(s) -
Li S.,
Marsaglia N.,
Garth C.,
Woodring J.,
Clyne J.,
Childs H.
Publication year - 2018
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13336
Subject(s) - lossless compression , lossy compression , computer science , reduction (mathematics) , data reduction , visualization , data visualization , data mining , point (geometry) , scientific visualization , data science , information visualization , theoretical computer science , algorithm , data compression , artificial intelligence , mathematics , geometry
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.