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008			211019s2022 si | s |||| 0|eng d
020			_a9789811640957
024	7		_a10.1007/978-981-16-4095-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A43 _b2022 EB
245	0	0	_aSublinear Computation Paradigm : _bAlgorithmic Revolution in the Big Data Era _cedited by Naoki Katoh, Yuya Higashikawa, Hiro Ito, Atsuki Nagao, Tetsuo Shibuya, Adnan Sljoka, Kazuyuki Tanaka, Yushi Uno
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (VIII, 410 páginas) _b92 ilustraciones, 42 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aChapter 1: What is the Sublinear Computation Paradigm? -- Chapter 2: Property Testing on Graphs and Games -- Chapter 3: Constant-Time Algorithms for Continuous Optimization Problems -- Chapter 4: Oracle-based Primal-Dual Algorithms for Packing and Covering Semidefinite Programs -- Chapter 5: Almost Linear Time Algorithms for Some Problems on Dynamic Flow Networks -- Chapter 6: Sublinear Data Structure -- Chapter 7: Compression and Pattern Matching -- Chapter 8: Orthogonal Range Search Data Structures -- Chapter 9: Enhanced RAM Simulation in Succinct Space -- Chapter 10: Review of Sublinear Modeling in Markov Random Fields by Statistical-Mechanical Informatics and Statistical Machine Learning Theory -- Chapter 11: Empirical Bayes Method for Boltzmann Machines -- Chapter 12: Dynamical analysis of quantum annealing -- Chapter 13: Mean-field analysis of Sourlas codes with adiabatic reverse annealing -- Chapter 14: Rigidity theory for protein function analysis and structural accuracy validations -- Chapter 15: Optimization of Evacuating and Walking Home Routes from Osaka City with Big Road Network Data on Nankai Megathrust Earthquake -- Chapter 16: Stream-based Lossless Data Compression.
506	0		_aOpen Access
520			_aThis 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.
988			_aSpringer_Computer_2022
650		7	_2embne _9151819 _aAlgoritmos computacionales
776	0	8	_iPrinted edition: _z9789811640940
776	0	8	_iPrinted edition: _z9789811640964
776	0	8	_iPrinted edition: _z9789811640971
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-4095-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eu _zSI