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oapen-20.500.12657-515012021-11-16T02:49:17Z Sublinear Computation Paradigm Katoh, Naoki Higashikawa, Yuya Ito, Hiro Nagao, Atsuki Shibuya, Tetsuo Sljoka, Adnan Tanaka, Kazuyuki Uno, Yushi Sublinear Algorithms polynomial time algorithms Constant-Time Algorithms Sublinear Computation Paradigm open access bic Book Industry Communication::U Computing & information technology::UM Computer programming / software development::UMB Algorithms & data structures bic Book Industry Communication::P Mathematics & science::PB Mathematics::PBK Calculus & mathematical analysis::PBKS Numerical analysis 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. 2021-11-15T15:28:29Z 2021-11-15T15:28:29Z 2022 book ONIX_20211115_9789811640957_31 9789811640957 https://library.oapen.org/handle/20.500.12657/51501 eng application/pdf Attribution 4.0 International 9789811640957.pdf https://www.springer.com/9789811640957 Springer Nature Springer Singapore 10.1007/978-981-16-4095-7 10.1007/978-981-16-4095-7 6c6992af-b843-4f46-859c-f6e9998e40d5 1f0de1ea-9a4f-46d5-a9ca-5bbc3290d8fe 9789811640957 Springer Singapore 410 [grantnumber unknown] Japan Science and Technology Agency 国立研究開発法人科学技術振興機構 open access
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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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