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Books > Computing & IT > General theory of computing > Data structures
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Sublinear Computation Paradigm - Algorithmic Revolution in the Big Data Era (Paperback, 1st ed. 2022)
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Sublinear Computation Paradigm - Algorithmic Revolution in the Big Data Era (Paperback, 1st ed. 2022)
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This open access book gives an overview of cutting-edge work on a
new paradigm called the "sublinear computation paradigm," which was
proposed in the large multiyear academic research project
"Foundations of Innovative Algorithms for Big Data." That project
ran from October 2014 to March 2020, in Japan. To handle the
unprecedented explosion of big data sets in research, industry, and
other areas of society, there is an urgent need to develop novel
methods and approaches for big data analysis. To meet this need,
innovative changes in algorithm theory for big data are being
pursued. For example, polynomial-time algorithms have thus far been
regarded as "fast," but if a quadratic-time algorithm is applied to
a petabyte-scale or larger big data set, problems are encountered
in terms of computational resources or running time. To deal with
this critical computational and algorithmic bottleneck, linear,
sublinear, and constant time algorithms are required.The sublinear
computation paradigm is proposed here in order to support
innovation in the big data era. A foundation of innovative
algorithms has been created by developing computational procedures,
data structures, and modelling techniques for big data. The project
is organized into three teams that focus on sublinear algorithms,
sublinear data structures, and sublinear modelling. The work has
provided high-level academic research results of strong
computational and algorithmic interest, which are presented in this
book. The book consists of five parts: Part I, which consists of a
single chapter on the concept of the sublinear computation
paradigm; Parts II, III, and IV review results on sublinear
algorithms, sublinear data structures, and sublinear modelling,
respectively; Part V presents application results. The information
presented here will inspire the researchers who work in the field
of modern algorithms.
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