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010   $a9783031435409
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024 7 $a10.1007/978-3-031-43540-9
035   $a(CKB)29526816200041
035   $a(MiAaPQ)EBC31093938
035   $a(Au-PeEL)EBL31093938
035   $a(DE-He213)978-3-031-43540-9
035   $a(OCoLC)1416899352
035   $a(EXLCZ)9929526816200041
100   $a20231230d2024      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aHypothesis Generation and Interpretation $eDesign Principles and Patterns for Big Data Applications /$fby Hiroshi Ishikawa
205   $a1st ed. 2024.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024.
215   $a1 online resource (380 pages)
225 1 $aStudies in Big Data,$x2197-6511 ;$v139
311 08$a9783031435393 
327   $aBasic Concept -- Hypothesis -- Science and Hypothesis -- Regression -- Machine Learning and Integrated Approach -- Hypothesis Generation by Difference -- Methods for Integrated Hypothesis Generation -- Interpretation.
330   $aThis book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques. The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on ?social infrastructure? applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases. The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns. Academic investigators and practitioners working on the further development and application of hypothesis generation and interpretation in big data computing, with backgrounds in data science and engineering, or the study of problem solving and scientific methods or who employ those ideas in fields like machine learning will find this book of considerable interest.
410  0$aStudies in Big Data,$x2197-6511 ;$v139
606   $aComputer science
606   $aDatabase management
606   $aData mining
606   $aMachine learning
606   $aBig data
606   $aSystem theory
606   $aTheory of Computation
606   $aDatabase Management
606   $aData Mining and Knowledge Discovery
606   $aMachine Learning
606   $aBig Data
606   $aComplex Systems
615  0$aComputer science.
615  0$aDatabase management.
615  0$aData mining.
615  0$aMachine learning.
615  0$aBig data.
615  0$aSystem theory.
615 14$aTheory of Computation.
615 24$aDatabase Management.
615 24$aData Mining and Knowledge Discovery.
615 24$aMachine Learning.
615 24$aBig Data.
615 24$aComplex Systems.
676   $a005.7
700   $aIshikawa$b Hiroshi$0303938
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910799235803321
996   $aHypothesis Generation and Interpretation$93874910
997   $aUNINA