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008			220113s2022 si | s |||| 0|eng d
020			_a9789811636073
024	7		_a10.1007/978-981-16-3607-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .B45 _b2022 EB
100	1		_aShi, Yong _eautor _9683260
245	1	0	_aAdvances in Big Data Analytics : _bTheory, Algorithms and Practices _cby Yong Shi
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XIV, 728 páginas) _b1 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
505	0		_aPart One: Concept and Theoretical Foundation -- Chapter 1: Big Data and Big Data Analytics -- Chapter 2: Multiple Criteria Optimization Classification -- Chapter 3: Support Vector Machine Classification -- Part Two: Functional Analysis -- Chapter 4: Feature Selection -- Chapter 5: Data Stream Analysis -- Chapter 6: Learning Analysis -- Chapter 7: Sentiment Analysis -- Chapter 8: Link Analysis -- Chapter 9: Evaluation Analysis -- Part Three: Application and Future Analysis -- Chapter 10: Business and Engineering Applications -- Chapter 11: Healthcare Applications -- Chapter 12: Artificial Intelligence IQ Test -- Chapter 13: Conclusions.
520			_aToday, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence. Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.
988			_aSpringer_Computer_2022
650		7	_2embne _9495511 _aDatos masivos
773	0		_tSpringer Nature eBook
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-3607-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2022 _dz _eu _zSI