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Sublinear algorithms for extreme-scale data analysis. (English) Zbl 1312.65009

Bennett, Janine (ed.) et al., Topological and statistical methods for complex data. Tackling large-scale, high-dimensional, and multivariate data spaces. Selected papers based on the presentations at the workshop on the analysis of large-scale, high-dimensional, and multivariate data using topology and statistics, Le Barp, France, June 12–14, 2013. Berlin: Springer (ISBN 978-3-662-44899-1/hbk; 978-3-662-44900-4/ebook). Mathematics and Visualization, 39-54 (2015).
Summary: The study of sublinear algorithms is a recent development in theoretical computer science and discrete mathematics that has significant potential to provide scalable analysis algorithms for massive data. The approaches of sublinear algorithms address the fundamental mathematical problem of understanding global features of a data set using limited resources. However, much of the work in this area is theoretical in nature and has yet to be applied to practical problems. This chapter provides background on sublinear algorithms, and then surveys a series of recent successes in the sublinear analysis of large-scale graphs and the robust generation of color maps for visualization of large physics simulation data. We end the chapter with a discussion of potential research directions.
For the entire collection see [Zbl 1304.54001].

MSC:

65C60 Computational problems in statistics (MSC2010)
65D19 Computational issues in computer and robotic vision
62-07 Data analysis (statistics) (MSC2010)
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