Big Data

Algorithms, Analytics, and Applications

Kuan-Ching Li, Hai Jiang, Laurence T. Yang , Alfredo Cuzzocrea

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English

Chapman & Hall/CRC
23 February 2015

Automatic control engineering; Computing: general; Databases

Series: Chapman & Hall/CRC Big Data Series

As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages.

Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections:

Big Data Management—considers the research issues related to the management of Big Data, including indexing and scalability aspects

Big Data Processing—addresses the problem of processing Big Data across a wide range of resource-intensive computational settings

Big Data Stream Techniques and Algorithms—explores research issues regarding the management and mining of Big Data in streaming environments

Big Data Privacy—focuses on models, techniques, and algorithms for preserving Big Data privacy

Big Data Applications—illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing

Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

Edited by: Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea
Imprint: Chapman & Hall/CRC
Country of Publication: United States
Dimensions: Height: 254mm, Width: 178mm, Spine: 36mm
Weight: 1.088kg
ISBN: 9781482240559
ISBN 10: 1482240556
Series: Chapman & Hall/CRC Big Data Series
Pages: 498
Publication Date: 23 February 2015
Audience: College/higher education , College/higher education , A / AS level , Further / Higher Education
Format: Hardback
Publisher's Status: Active

Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea

Reviews for Big Data: Algorithms, Analytics, and Applications

The collection presented in the book covers fundamental and realistic issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields. ... This book is required understanding for anyone working in a major field of science, engineering, business, and financing. -Jack Dongarra, University of Tennessee The editors have assembled an impressive book consisting of 22 chapters written by 57 authors from 12 countries across America, Europe, and Asia. ... This book has great potential to provide fundamental insight and privacy to individuals, long-lasting value to organizations, and security and sustainability to the cyber-physical-social ecosystem .... -D. Frank Hsu, Fordham University These editors are active researchers and have done a lot of work in the area of Big Data. They assembled a group of outstanding chapter authors. ... Each section contains several case studies to demonstrate how the related issues are addressed. ... I highly recommend this timely and valuable book. I believe that it will benefit many readers and contribute to the further development of Big Data research. -Dr. Yi Pan, Georgia State University