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020 _a9783031435409
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024 7 _a10.1007/978-3-031-43540-9
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_223
100 1 _aIshikawa, Hiroshi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_929891
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
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_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.
650 0 _aComputer science.
_99832
650 0 _aDatabase management.
_93157
650 0 _aData mining.
_93907
650 0 _aMachine learning.
_91831
650 0 _aBig data.
_94174
650 0 _aSystem theory.
_93409
650 1 4 _aTheory of Computation.
_995787
650 2 4 _aDatabase Management.
_93157
650 2 4 _aData Mining and Knowledge Discovery.
_995789
650 2 4 _aMachine Learning.
_91831
650 2 4 _aBig Data.
_94174
650 2 4 _aComplex Systems.
_918136
710 2 _aSpringerLink (Online service)
_995792
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
_995793
856 4 0 _uhttps://doi.org/10.1007/978-3-031-43540-9
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912 _aZDB-2-SXCS
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