04047nam a22006135i 4500 978-3-031-43540-9 DE-He213 20251030110904.0 cr nn 008mamaa 231230s2024 sz | s |||| 0|eng d 9783031435409 978-3-031-43540-9 10.1007/978-3-031-43540-9 doi QA75.5-76.95 UYA bicssc COM014000 bisacsh UYA thema Ishikawa, Hiroshi. author. aut http://id.loc.gov/vocabulary/relators/aut 68723 Hypothesis Generation and Interpretation [electronic resource] : Design Principles and Patterns for Big Data Applications / by Hiroshi Ishikawa. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XII, 372 p. 177 illus., 125 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Big Data, 2197-6511 ; 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. Computer science. 58552 Database management. 60571 Data mining. 6941 Machine learning. 55059 Big data. 57216 System theory. 66519 Theory of Computation. 67552 Database Management. 60571 Data Mining and Knowledge Discovery. Machine Learning. 55059 Big Data. 57216 Complex Systems. 66520 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031435393 Printed edition: 9783031435416 Printed edition: 9783031435423 Studies in Big Data, 2197-6511 ; 139 68724 https://doi.org/10.1007/978-3-031-43540-9 ZDB-2-SCS ZDB-2-SXCS 56952 56952