OXFORD UNIVERSITY PRESS

Big Data: A Very Short Introduction

ISBN : 9780198779575

参考価格(税込): 
¥1,404
著者: 
Dawn E. Holmes
ページ
136 ページ
フォーマット
Paperback
サイズ
111 x 174 mm
刊行日
2017年11月
シリーズ
Very Short Introductions

カートに入れる

購入するには、商品をカートに入れ、ページ上方の「ショッピングカート」より手続きを行ってください。

メール送信
印刷
  • Introduces the topic of big data, drawing on the fields of statistics, probability, and computer science
  • Illustrates the power of big data in everyday life, and the attendant security risks
  • Analyses the special techniques required for the storage and analysis of big data
  • Discusses the use of big data by companies and businesses today

 
Since long before computers were even thought of, data has been collected and organized by diverse cultures across the world. Once access to the Internet became a reality for large swathes of the world's population, the amount of data generated each day became huge, and continues to grow exponentially. It includes all our uploaded documents, video, and photos, all our social media traffic, our online shopping, even the GPS data from our cars.

'Big Data' represents a qualitative change, not simply a quantitative one. The term refers both to the new technologies involved, and to the way it can be used by business and government. Dawn E. Holmes uses a variety of case studies to explain how data is stored, analysed, and exploited by a variety of bodies from big companies to organizations concerned with disease control. Big data is transforming the way businesses operate, and the way medical research can be carried out. At the same time, it raises important ethical issues; Holmes discusses cases such as the Snowden affair, data security, and domestic smart devices which can be hijacked by hackers. 

目次: 

1: The data explosion
2: Why is big data special?
3: Storing big data
4: Big data analysis
5: Big data and medicine
6: Big data, big business
7: Big data security and the Snowden case
8: The internet of things
Byte size chart
References
Further Reading
Index

著者について: 

Dawn E. Holmes, Faculty Member, Department of Statistics and Applied Probability, University of California, Santa Barbara
 
Dawn Holmes is a faculty member in the Department of Statistics and Applied Probability at the University of California, Santa Barbara, specializing in Bayesian networks, machine learning, and data mining. She is the co-editor of a three-volume work, Data Mining: Foundations and Intelligent Paradigms (Springer, 2014), and Associate Editor of the International Journal of Knowledge-Based and Intelligent Information Systems.

"Big data is in the news, and this excellent very short introduction brings the reader up to speed and enables them to understand the various components and implications." - Paradigm Explorer

"This is a very useful, concise introduction to the topic of big data." - Jonathan Cowie, Science Fact & Science Fiction Concatenation

"A very short introduction to a very big subject ... arguably the most topical of this book series ... This very short introduction is perfect for anyone who is a little bit baffled by the very concept of big data. Holmes introduces the subject in a format that is both concise and manageable." - Jade Taylor-Salazar, E&T Magazine

このページに掲載の「参考価格」は日本国内における希望小売価格です。当ウェブサイトでのご購入に対して特別価格が適用される場合、販売価格は「割引価格」として表示されます。なお、価格は予告なく変更されることがございますので、あらかじめご了承ください。