Hypothesis Generation and Interpretation by Hiroshi Ishikawa (.PDF)

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Hypothesis Generation and Interpretation: Design Principles and Patterns for Big Data Applications by Hiroshi Ishikawa
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Overview: 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. In the Big Data era, characterized by volume, variety, and velocity, which generates a large amount of diverse data at high speed, the role of a hypothesis is more important to generate the final value, and such a hypothesis is more complicated and complex than ever. At the same time, the era of Big Data creates new vague concerns for end users as to whether Big Data relevant to them will be used appropriately.
Genre: Non-Fiction > Tech & Devices

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