Ghost Data
Ghost data is the data that is not there (invisible), and there are many types.[1] [2][3]
Data type[edit]
In physics (quantum field theory), a ghost, ghost field, or gauge ghost is an unphysical state in a gauge theory. Ghosts are necessary to keep gauge invariance in theories where the local fields exceed a number of physical degrees of freedom. Analogously, how to (gauge) deal with ghost data (invisible, not visualized or not recognized) is still challenging data scientists. The common example of ghost data is virtual, sparse, missing, or pretend data[4]. One example is the evidence gap in global health research and related research on the issue of counterfeit drugs. Global health research and policies have warned about the increasing threat of counterfeit and inferior drugs [5]. In addition to missing data, ghost data also includes other invisible data. These data may be the one that some people can perceive but others cannot, such as survival as a digital ghost[6], ghost images [7][8], digital museum and archive [9].
Data processing[edit]
Replicated along the external boundaries, ghost data [1] lets data-parallel visualization algorithms operate correctly and without further communication. A classic example of this is isosurface extraction (also called isocontouring). Ghost data lets the interpolation be consistent on both sides of the boundary, thereby ensuring a crack-free surface. Ghost data's uses aren't limited to isocontouring. Ghost data is also needed for gradient calculations, reconstructing material interfaces in Eulerian hydrodynamics simulations, smoothing out scalars for volume renderings with pseudocolors, and many other computations involving neighbor data. The cost of storing ghost data is felt by all parties: during write (for the simulation code) and read (for the simulation code or the visualization code), and especially in the footprint on the disk. One layer of ghost data suffices for all subsequent visualization and analysis.
See also[edit]
References[edit]
- ↑ 1.0 1.1 Martin Isenburg, Peter Lindstrom, Hank Childs (2010). "Parallel and Streaming Generation of Ghost Data for Structured Grids". IEEE Computer Graphics and Applications. 30 (3): 32–44. doi:10.1109/MCG.2010.26. PMID 20650716. Unknown parameter
|s2cid=ignored (help) - ↑ "What is ghost data?".
- ↑ "Dennis Lin, Ghost Data,Department of Statistics,The Pennsylvania State University" (PDF).
- ↑ Valente, Daniel. "4 ways scientists and engineers create ghost data and why that's a good thing". JMP User Community. jmp. Retrieved 2020-10-02.
- ↑ Sarah Hodges, Emma Garnett (2020). "The ghost in the data: Evidence gaps and the problem of fake drugs in global health research". Global Public Health. 15 (8): 1103–1118. doi:10.1080/17441692.2020.1744678. PMC 7446034 Check
|pmc=value (help). PMID 32228359 Check|pmid=value (help). - ↑ Eric Steinhart (2007). "Survival as a Digital Ghost". Minds & Machines. 17 (3): 261–271. doi:10.1007/s11023-007-9068-0. Unknown parameter
|s2cid=ignored (help) - ↑ R. I. Khakimov, B. M. Henson, D. K. Shin, S. S. Hodgman, R. G. Dall, K. G. H. Baldwin & A. G. Truscott (30 November 2016). "Ghost imaging with atoms". Nature. 540 (7631): 100–103. arXiv:1607.02240. Bibcode:2016Natur.540..100K. doi:10.1038/nature20154. PMID 27905444. Retrieved 4 October 2020. Unknown parameter
|s2cid=ignored (help) - ↑ David S. Simon, Gregg JaegerAlexander V. Sergienko (4 November 2016). "Ghost Imaging and Related Topics". Quantum Metrology, Imaging, and Communication. Quantum Science and Technology. springer. p. 131-158. doi:10.1007/978-3-319-46551-7_6. ISBN 978-3-319-46549-4. Retrieved 4 October 2020. Search this book on
- ↑ Grau, Oliver (2017). Museum and archive on the move: changing cultural institutions in the digital era. Walter de Gruyter GmbH & Co KG. ISBN 9783110529630. Search this book on
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