東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Hypothesis generation and interpretation = design principles and patterns for big data applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hypothesis generation and interpretation/ by Hiroshi Ishikawa.
Reminder of title:	design principles and patterns for big data applications /
Author:	Ishikawa, Hiroshi.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xii, 372 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Basic Concept -- Hypothesis -- Science and Hypothesis -- Regression -- Machine Learning and Integrated Approach -- Hypothesis Generation by Difference -- Methods for Integrated Hypothesis Generation -- Interpretation.
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-3-031-43540-9
ISBN:	9783031435409

Hypothesis generation and interpretation = design principles and patterns for big data applications /
Ishikawa, Hiroshi.

Hypothesis generation and interpretationdesign principles and patterns for big data applications /[electronic resource] :by Hiroshi Ishikawa. - Cham :Springer International Publishing :2024. - xii, 372 p. :ill. (some col.), digital ;24 cm. - Studies in big data,v. 1392197-6511 ;. - Studies in big data ;v. 139..

Basic Concept -- Hypothesis -- Science and Hypothesis -- Regression -- Machine Learning and Integrated Approach -- Hypothesis Generation by Difference -- Methods for Integrated Hypothesis Generation -- Interpretation.

This 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.

ISBN: 9783031435409

Standard No.: 10.1007/978-3-031-43540-9doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Hypothesis generation and interpretation = design principles and patterns for big data applications /
LDR:02899nmm a2200337 a 4500 001 2367608
003 DE-He213
005 20231230130400.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031435409 $q (electronic bk.)
020 $a 9783031435393 $q (paper)
024 7 $a 10.1007/978-3-031-43540-9 $2 doi
035 $a 978-3-031-43540-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a UYA $2 bicssc
072 7 $a COM014000 $2 bisacsh
072 7 $a UYA $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b I79 2024
100 1 $a Ishikawa, Hiroshi. $3 3411935
245 1 0 $a Hypothesis generation and interpretation $h [electronic resource] : $b design principles and patterns for big data applications / $c by Hiroshi Ishikawa.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xii, 372 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6511 ; $v v. 139
505 0 $a Basic Concept -- Hypothesis -- Science and Hypothesis -- Regression -- Machine Learning and Integrated Approach -- Hypothesis Generation by Difference -- Methods for Integrated Hypothesis Generation -- Interpretation.
520 $a This 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 $a Big data. $3 2045508
650 0 $a Automatic hypothesis formation. $3 3710843
650 1 4 $a Theory of Computation. $3 892514
650 2 4 $a Database Management. $3 891010
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Big Data. $3 3134868
650 2 4 $a Complex Systems. $3 1566441
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in big data ; $v v. 139. $3 3710842
856 4 0 $u https://doi.org/10.1007/978-3-031-43540-9
950 $a Computer Science (SpringerNature-11645)