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008			231230s2024 sz | o |||| 0|eng d
020			_a9783031435409
024	7		_a10.1007/978-3-031-43540-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D343 _b2024 EB
100	1		_aIshikawa, Hiroshi _d1956- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9689348
245	1	0	_aHypothesis Generation and Interpretation _b: Design Principles and Patterns for Big Data Applications _cby Hiroshi Ishikawa
250			_afirst edition 2024
264		1	_aCham _c2024 _bSpringer International Publishing
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
490	0		_aStudies in Big Data _x2197-6511 ; _v139
505	0		_aBasic Concept -- Hypothesis -- Science and Hypothesis -- Regression -- Machine Learning and Integrated Approach -- Hypothesis Generation by Difference -- Methods for Integrated Hypothesis Generation -- Interpretation.
520			_aThis book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques. The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on "social infrastructure" applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases. The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns. Academic investigators and practitioners working on the further development and application of hypothesis generation and interpretation in big data computing, with backgrounds in data science and engineering, or the study of problem solving and scientific methods or who employ those ideas in fields like machine learning will find this book of considerable interest.
988			_aSpringer_Computer_2024
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9156434 _aProceso distribuido (Informática)
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-43540-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _ejc _zSI