Hypothesis Generation and Interpretation: Design Principles and Patterns for Big Data Applications

Ishikawa, Hiroshi

Hypothesis Generation and Interpretation: Design Principles and Patterns for Big Data Applications
  • 出版商: Springer
  • 出版日期: 2024-02-02
  • 售價: $7,150
  • 貴賓價: 9.5 折 $6,793
  • 語言: 英文
  • 頁數: 372
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031435397
  • ISBN-13: 9783031435393
  • 相關分類: 大數據 Big-data

    • 海外代購書籍(需單獨結帳)

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商品描述

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.

商品描述(中文翻譯)

這本書詳細探討了數據科學和數據分析，並強調在大數據應用設計中數據工程和數據管理的重要性。作者利用在一系列大數據應用中發現的模式，提供了假設生成、整合大數據處理和管理、機器學習和數據挖掘技術的設計原則。

本書提出並解釋了創新的原則，通過將微觀解釋（基於分析模型和其中的個別決策的解釋）與宏觀解釋（基於應用過程和模型生成的解釋）相結合，來解釋假設。通過實際案例研究，展示了假設生成和解釋技術的工作原理。這些案例基於“社會基礎設施”應用，如入境旅遊、災害管理、月球和行星探索以及傳染病治療。

《假設生成和解釋》提出的新方法和技術通過納入科學的歷史觀點，強調了其設計原則和模式背後的思想的起源和發展。

從事大數據計算中假設生成和解釋的進一步發展和應用的學術研究人員和從業人員，背景涉及數據科學和工程，或者研究問題解決和科學方法，或者在機器學習等領域應用這些思想的人，將對本書感興趣。

作者簡介

​Hiroshi Ishikawa received the B.S. and Ph.D degrees in Information Science from the University of Tokyo. After working for Fujitsu Laboratories and being a full professor of Shizuoka University, he was a full professor of Tokyo Metropolitan University (TMU) until March, 2021. He is now a distinguished leading professor and an emeritus professor of TMU. He is also the director of TMU Social Big Data Research Center. His research interests include databases, data mining, social media, and big data.

He has published actively in international, refereed journals and conferences, such as ACM Transactions on Database Systems, IEEE Transactions on Knowledge and Data Engineering, The VLDB Journal, IEEE International Conference on Data Engineering, and ACM SIGSPATIAL and Management of Emergent Digital EcoSystems (MEDES). He has authored and co-authored a dozen books, including Social Big Data Mining (CRC, 2015) and Object-Oriented Database System (Springer-Verlag, 1993).

He received the Sakai Memorial Distinguished Award from the Information Processing Society of Japan (IPSJ) in 1994, Commendation by the Director General of Science and Technology Agency of Japan in 1997, Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology of Japan in 2021, and Commendation from the Database Society of Japan in 2022. He was twice an invited professor at the Polytechnic School of the University of Nantes, France. He was a trustee board member of the Database Society of Japan, an editorial board member of The VLDB Journal, the chairman of the SIG on Database Systems of IPSJ, and an editor-in-chief of IPSJ Trans. on Databases. He is a co-founder of ACM MEDES conference. He is a Fellow of the IPSJ and the Institute of Electronics, Information and Communication Engineers (IEICE) and a member of both the ACM and the IEEE.

作者簡介(中文翻譯)

Hiroshi Ishikawa在東京大學獲得資訊科學的學士和博士學位。在任職於富士通實驗室並擔任靜岡大學的教授後，他成為東京都立大學（TMU）的教授，直到2021年3月。他現在是TMU的傑出領先教授和名譽教授，也是TMU社會大數據研究中心的主任。他的研究興趣包括數據庫、數據挖掘、社交媒體和大數據。

他在國際上的著名期刊和會議上積極發表論文，例如《ACM Transactions on Database Systems》、《IEEE Transactions on Knowledge and Data Engineering》、《The VLDB Journal》、IEEE國際數據工程大會以及ACM SIGSPATIAL和Management of Emergent Digital EcoSystems（MEDES）。他撰寫和合著了十幾本書，包括《Social Big Data Mining》（CRC，2015）和《Object-Oriented Database System》（Springer-Verlag，1993）。

他於1994年獲得日本信息處理學會（IPSJ）的酒井紀念傑出獎，1997年獲得日本科學技術廳廳長表彰，2021年獲得日本文部科學省科學技術表彰，以及2022年獲得日本數據庫學會表彰。他曾兩次應邀擔任法國南特大學理工學院的客座教授。他曾擔任日本數據庫學會的董事會成員，是《The VLDB Journal》的編輯委員會成員，IPSJ數據庫系統SIG的主席，以及《IPSJ Trans. on Databases》的主編。他是ACM MEDES會議的共同創辦人。他是IPSJ和電子情報通信學會（IEICE）的會士，也是ACM和IEEE的成員。

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