高雄醫學大學圖書館 |

Handbook of research on big data storage and visualization techniques

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Handbook of research on big data storage and visualization techniques/ Richard S. Segall and Jeffrey S. Cook, editors.
其他題名:	Big data storage and visualization techniques
其他作者:	Segall, Richard,
出版者:	Hershey, Pennsylvania :IGI Global, : [2018],
面頁冊數:	1 online resource (2 v.)
提要註:	"This book presents the concepts of big data, explores its analytics and technologies and their applications and develops an understanding of issues pertaining to the use of big data in multidisciplinary fields. It explores big data through the historical and technical background, architecture, open-source and commercial programming systems, analytics, state of practice in industry, and other topics"--
標題:	Big data - Congresses. -
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3142-5
ISBN:	9781522531432 (ebook)

Handbook of research on big data storage and visualization techniques
Handbook of research on big data storage and visualization techniques[electronic resource] /Big data storage and visualization techniquesRichard S. Segall and Jeffrey S. Cook, editors. - Hershey, Pennsylvania :IGI Global,[2018] - 1 online resource (2 v.)

Includes bibliographical references and index.

Volume I. Section 1. Introduction to big data and storage systems. Chapter 1. Overview of big data and its visualization ; Chapter 2. Overview of big-data-intensive storage and its technologies -- Section 2. Big data technologies and architectural patterns. Chapter 3. Database systems for big data storage and retrieval ; Chapter 4. Hadoop framework for handling big data needs ; Chapter 5. Role of open source software in big data storage -- Section 3. Big data in clouds, clusters, and grids. Chapter 6. Big data tools for computing on clouds and grids ; Chapter 7. A review of security challenges in cloud storage of big data ; Chapter 8. Architecture for big data storage in different cloud deployment models -- Section 4. Big data processing for storage and visualization. Chapter 9. Programming and pre-processing systems for big data storage and visualization ; Chapter 10. High performance storage for big data analytics and visualization ; Chapter 11. Big data in massive parallel processing: a multi-core processors perspective ; Chapter 12. Distributed streaming big data analytics for internet of things (IoT) -- Section 5. Applications of big data storage. Chapter 13. Scalable data warehouse architecture: a higher education case study ; Chapter 14. Resource provisioning and scheduling of big data processing jobs ; Chapter 15. Issues and methods for access, storage, and analysis of data from online social communities ; Chapter 16. Big data storage for the modeling of historical time series solar irradiations -- Volume II. Section 6. Visualization tools and techniques. Chapter 17. Big data visualization tools and techniques ; Chapter 18. The image as big data toolkit: an application case study in image analysis, feature recognition, and data visualization ; Chapter 19. Statistical visualization of big data through hadoop streaming in rstudio ; Chapter 20. Visualization of big data sets using computer graphics ; Chapter 21. Visualization of predictive modeling for big data using various approaches when there are rare events at differing levels ; Chapter 22. Introduction to smart city and agricultural revolution: big data and internet of things (IoT) ; Chapter 23. Mining multimodal big data: tensor methods and applications -- Section 7. Applications of big data visualization. Chapter 24. Big data and its role in facilitating the visualization of financial analytics ; Chapter 25. Visualization and storage of big data for linguistic applications ; Chapter 26. Big data analysis techniques for visualization of genomics in medicinal plants ; Chapter 27. The artist's move: the discipline of dance and big data ; Chapter 28. Visualizing big data from a philosophical perspective ; Chapter 29. Big data analytics and visualization of performance of Stock exchange companies based on balanced scorecard indicators ; Chapter 30. Visualization tools for big data analytics in quantitative chemical analysis: a tutorial in chemometrics.

Restricted to subscribers or individual electronic text purchasers.

"This book presents the concepts of big data, explores its analytics and technologies and their applications and develops an understanding of issues pertaining to the use of big data in multidisciplinary fields. It explores big data through the historical and technical background, architecture, open-source and commercial programming systems, analytics, state of practice in industry, and other topics"--

ISBN: 9781522531432 (ebook)Subjects--Topical Terms:

408058
Big data
--Congresses.

LC Class. No.: QA76.9.B45 / H37 2018e

Dewey Class. No.: 005.7

Handbook of research on big data storage and visualization techniques
LDR:04449nam a2200289 a 4500 001 314562
003 IGIG
005 20181029090040.0
006 m o d
007 cr cn
008 181108s2017 pau fobf 001 0 eng d
010 $z 2017016107
020 $a 9781522531432 (ebook)
020 $a 9781522531425 (hardcover)
035 $a (OCoLC)1022575719
035 $a 1071025351
040 $a CaBNVSL $b eng $c CaBNVSL $d CaBNVSL
050 4 $a QA76.9.B45 $b H37 2018e
082 0 4 $a 005.7 $2 23
245 0 0 $a Handbook of research on big data storage and visualization techniques $h [electronic resource] / $c Richard S. Segall and Jeffrey S. Cook, editors.
246 3 0 $a Big data storage and visualization techniques
260 $a Hershey, Pennsylvania : $b IGI Global, $c [2018]
300 $a 1 online resource (2 v.)
504 $a Includes bibliographical references and index.
505 0 $a Volume I. Section 1. Introduction to big data and storage systems. Chapter 1. Overview of big data and its visualization ; Chapter 2. Overview of big-data-intensive storage and its technologies -- Section 2. Big data technologies and architectural patterns. Chapter 3. Database systems for big data storage and retrieval ; Chapter 4. Hadoop framework for handling big data needs ; Chapter 5. Role of open source software in big data storage -- Section 3. Big data in clouds, clusters, and grids. Chapter 6. Big data tools for computing on clouds and grids ; Chapter 7. A review of security challenges in cloud storage of big data ; Chapter 8. Architecture for big data storage in different cloud deployment models -- Section 4. Big data processing for storage and visualization. Chapter 9. Programming and pre-processing systems for big data storage and visualization ; Chapter 10. High performance storage for big data analytics and visualization ; Chapter 11. Big data in massive parallel processing: a multi-core processors perspective ; Chapter 12. Distributed streaming big data analytics for internet of things (IoT) -- Section 5. Applications of big data storage. Chapter 13. Scalable data warehouse architecture: a higher education case study ; Chapter 14. Resource provisioning and scheduling of big data processing jobs ; Chapter 15. Issues and methods for access, storage, and analysis of data from online social communities ; Chapter 16. Big data storage for the modeling of historical time series solar irradiations -- Volume II. Section 6. Visualization tools and techniques. Chapter 17. Big data visualization tools and techniques ; Chapter 18. The image as big data toolkit: an application case study in image analysis, feature recognition, and data visualization ; Chapter 19. Statistical visualization of big data through hadoop streaming in rstudio ; Chapter 20. Visualization of big data sets using computer graphics ; Chapter 21. Visualization of predictive modeling for big data using various approaches when there are rare events at differing levels ; Chapter 22. Introduction to smart city and agricultural revolution: big data and internet of things (IoT) ; Chapter 23. Mining multimodal big data: tensor methods and applications -- Section 7. Applications of big data visualization. Chapter 24. Big data and its role in facilitating the visualization of financial analytics ; Chapter 25. Visualization and storage of big data for linguistic applications ; Chapter 26. Big data analysis techniques for visualization of genomics in medicinal plants ; Chapter 27. The artist's move: the discipline of dance and big data ; Chapter 28. Visualizing big data from a philosophical perspective ; Chapter 29. Big data analytics and visualization of performance of Stock exchange companies based on balanced scorecard indicators ; Chapter 30. Visualization tools for big data analytics in quantitative chemical analysis: a tutorial in chemometrics.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a "This book presents the concepts of big data, explores its analytics and technologies and their applications and develops an understanding of issues pertaining to the use of big data in multidisciplinary fields. It explores big data through the historical and technical background, architecture, open-source and commercial programming systems, analytics, state of practice in industry, and other topics"-- $c Provided by publisher.
650 0 $a Big data $v Congresses. $3 408058
650 0 $a Information visualization, $v Handbooks, manuals, etc. $3 433676
700 1 $a Segall, Richard, $d 1949- $e editor. $3 408023
700 1 $a Cook, Jeffrey S., $d 1966- $e editor. $3 408024
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-3142-5