大葉大學圖書館 |

Big data concepts, theories, and applications[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	005.7
書名/作者:	Big data concepts, theories, and applications/ edited by Shui Yu, Song Guo.
其他作者:	Guo, Song.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	viii, 437 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Big data.
標題:	Computer Science.
標題:	Information Systems and Communication Service.
標題:	Systems and Data Security.
標題:	Computer Communication Networks.
ISBN:	9783319277639
ISBN:	9783319277615
內容註:	Big Continuous Data: Dealing with Velocity by Composing Event Streams -- Big Data Tools and Platforms -- Traffic Identification in Big Internet Data -- Security Theories and Practices for Big Data -- Rapid Screening of Big Data against Inadvertent Leaks -- Big Data Storage Security -- Cyber Attacks on MapReduce Computation Time in a Hadoop Cluster -- Security and Privacy for Big Data -- Big Data Applications in Engineering and Sciences -- Geospatial Big Data for Environmental and Agricultural Applications -- Big Data in Finance -- Big Data Applications in Business Analytics.
摘要、提要註:	This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.
電子資源:	http://dx.doi.org/10.1007/978-3-319-27763-9

Big data concepts, theories, and applications[electronic resource] /
Big data concepts, theories, and applications[electronic resource] /edited by Shui Yu, Song Guo. - Cham :Springer International Publishing :2016. - viii, 437 p. :ill., digital ;24 cm.

Big Continuous Data: Dealing with Velocity by Composing Event Streams -- Big Data Tools and Platforms -- Traffic Identification in Big Internet Data -- Security Theories and Practices for Big Data -- Rapid Screening of Big Data against Inadvertent Leaks -- Big Data Storage Security -- Cyber Attacks on MapReduce Computation Time in a Hadoop Cluster -- Security and Privacy for Big Data -- Big Data Applications in Engineering and Sciences -- Geospatial Big Data for Environmental and Agricultural Applications -- Big Data in Finance -- Big Data Applications in Business Analytics.

This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.

ISBN: 9783319277639

Standard No.: 10.1007/978-3-319-27763-9doiSubjects--Topical Terms:

571002
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Big data concepts, theories, and applications[electronic resource] /
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520 $a This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.
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