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001			978-3-031-43540-9
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008			231230s2024 sz | s |||| 0|eng d
020			_a9783031435409 _9978-3-031-43540-9
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100	1		_aIshikawa, Hiroshi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aHypothesis Generation and Interpretation _h[electronic resource] : _bDesign Principles and Patterns for Big Data Applications / _cby Hiroshi Ishikawa.
250			_a1st ed. 2024.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2024.
300			_aXII, 372 p. 177 illus., 125 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_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.
541			_fUABC ; _cPerpetuidad
650		0	_aComputer science.
650		0	_aDatabase management.
650		0	_aData mining.
650		0	_aMachine learning.
650		0	_aBig data.
650		0	_aSystem theory.
650	1	4	_aTheory of Computation.
650	2	4	_aDatabase Management.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aMachine Learning.
650	2	4	_aBig Data.
650	2	4	_aComplex Systems.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031435393
776	0	8	_iPrinted edition: _z9783031435416
776	0	8	_iPrinted edition: _z9783031435423
830		0	_aStudies in Big Data, _x2197-6511 ; _v139
856	4	0	_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-43540-9
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cLIBRO_ELEC
999			_c273858 _d273857