Sublinear algorithms for extreme-scale data analysis

Abstract

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.