Big Data Management and Processing: 1st Edition (Hardback) book cover

Big Data Management and Processing

1st Edition

Edited by Kuan-Ching Li, Hai Jiang, Albert Y. Zomaya

Chapman and Hall/CRC

469 pages | 100 B/W Illus.

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Hardback: 9781498768078
pub: 2017-05-25
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pub: 2017-05-19
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Description

From the Foreword:

"Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications… [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies."

---Sartaj Sahni, University of Florida, USA

"Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields.

--Hai Jin, Huazhong University of Science and Technology, China

Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems.

The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions.

The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.

Table of Contents

1 Big Data: Legal Compliance and Quality Management

Paolo Balboni and Theodora Dragan

2 EnergyManagement for Green Big Data Centers

Chonglin Gu, Hejiao Huang, and Xiaohua Jia

3 The Art of In-Memory Computing for Big Data Processing

Mihaela-Andreea Vasile and Florin Pop

4 Scheduling Nested Transactions on In-Memory Data Grids

Junwhan Kim, Roberto Palmieri, and Binoy Ravindran

5 Co-Scheduling High-Performance Computing Applications

Guillaume Aupy, Anne Benoit, Loic Pottier, Padma Raghavan, Yves Robert, and Manu Shantharam

6 ResourceManagement forMapReduce Jobs Performing Big Data Analytics

Norman Lim and Shikharesh Majumdar

7 Tyche: An Efficient Ethernet-Based Protocol for Converged Networked Storage

Pilar Gonz´alez-F´erez and Angelos Bilas

8 Parallel Backpropagation Neural Network for Big Data Processing on Many-Core Platform

Boyang Li and Chen Liu

9 SQL-on-Hadoop Systems: State-of-the-Art Exploration, Models, Performances, Issues, and Recommendations

Alfredo Cuzzocrea, Rim Moussa, and Soror Sahri

10 One Platform Rules All: From Hadoop 1.0 to Hadoop 2.0 and Spark

Xiongpai Qin and Keqin Li

11 Security, Privacy, and Trust for User-Generated Content: The Challenges and Solutions

Yuhong Liu, Yu Wang, and Nam Ling

12 Role of Real-Time Big Data Processing in the Internet of Things

Miyuru Dayarathna, Paul Fremantle, Srinath Perera, and Sriskandarajah Suhothayan

13 End-to-End Security Framework for Big Sensing Data Streams

Deepak Puthal, Surya Nepal, Rajiv Ranjan, and Jinjun Chen

14 Considerations on the Use of Custom Accelerators for Big Data Analytics

Vito Giovanni Castellana, Antonino Tumeo, Marco Minutoli, Marco Lattuada, and Fabrizio Ferrandi

15 Complex Mining from Uncertain Big Data in Distributed Environments: Problems, Definitions, and Two Effective and Efficient Algorithms

Alfredo Cuzzocrea, Carson Kai-Sang Leung, Fan Jiang, and Richard Kyle MacKinnon

16 Clustering in Big Data

Min Chen, Simone A. Ludwig, and Keqin Li

17 Large Graph Computing Systems

Chengwen Wu, Guangyan Zhang, Keqin Li, and Weimin Zheng

18 Big Data in Genomics

Huaming Chen, Jiangning Song, Jun Shen, and Lei Wang

19 Maximizing the Return on Investment in Big Data Projects: An Approach Based upon the Incremental Funding of Project Development

Antonio Juarez Alencar, Mauro Penha Bastos, Eber Assis Schmitz, Monica Ferreira da Silva, and Petros Sotirios Stefaneas

20 Parallel DataMining and Applications in Hospital Big Data Processing

Jianguo Chen, Zhuo Tang, Kenli Li, and Keqin Li

21 Big Data in the Parking Lot

Ryan Florin, Syedmeysam Abolghasemi, Aida Ghazi Zadeh, and Stephan Olariu

About the Editors

Kuan-Ching Li is a professor in the Department of Computer Science and Information Engineering at Providence University, Taiwan. Dr. Li is recipient of awards from Nvidia, Ministry of Education (MOE)/Taiwan, Ministry of Science and Technology (MOST)/Taiwan, as well as from a number of industrial companies. He has also received guest and distinguished chair professorships from universities in China and other countries. Dr. Li has been involved actively in conferences and workshops as a program/general/steering conference chairman positions, numerous conferences and workshops as a program committee member, and has organized numerous conferences related to high-performance computing and computational science & engineering.

Dr. Li is the Editor-in-Chief of technical publications International Journal of Computational Science and Engineering (IJCSE), International Journal of Embedded Systems (IJES) and International Journal of High Performance Computing and Networking (IJHPCN), all published by Inderscience, also serving a number of journal’s editorial boards and guest editorships. In addition, he is author or editor of several technical professional books published by CRC Press, Springer, McGraw-Hill and IGI Global. His topics of interest include GPU/Manycore computing, Big Data and Cloud. Dr. Li is a member of Taiwan Association of Cloud Computing (TACC), a Senior Member of the IEEE and a Fellow of the IET.

Hai Jiang is a professor in the Department of Computer Science at Arkansas State University, USA. He received his B.S. degree from Beijing University of Posts and Telecommunications, China, M.A. and Ph.D. degrees from Wayne State University, Detroit, MI, USA. His current research interests include Parallel & Distributed Systems, Computer & Network Security, High Performance Computing and Communication, Big Data, and Modeling & Simulation. He has published one book and research papers in major international journals and conference proceedings. He has served as a U.S. National Science Foundation proposal review panelists and a U.S. DoE (Department of Energy) Smart Grid Investment Grant (SGIG) reviewer multiple times. He serves as an editor for International Journal of High Performance Computing and Networking (IJHPCN), a regional editor for International Journal of Computational Science and Engineering (IJCSE) as well as International Journal of Embedded Systems (IJES), an editorial board member for International Journal of Big Data Intelligence (IJBDI), the Scientific World Journal (TSWJ), Open Journal of Internet of Things (OJIOT) and GSTF Journal on Social Computing (JSC), a guest editor for IEEE Systems Journal, International Journal of Ad Hoc and Ubiquitous Computing, Cluster Computing and The Scientific World Journal for multiple special issues. He has also served as a General Chair or Program Chair for some major conferences/workshops (CSE, HPCC, ISPA, GPC, ScaleCom, ESCAPE, GPU-Cloud, FutureTech, GPUTA, FC, SGC). He has been involved in 90 conferences and workshops as a session chair or as a program committee member, including major conferences such as AINA, ICPP, IUCC, ICPADS, TrustCom, HPCC, GPC, EUC, ICIS, SNPD, TSP, PDSEC, SECRUPT, and ScalCom. He has reviewed six Cloud-Computing related books (Distributed and Cloud Computing, Virtual Machines, Cloud Computing: Theory and Practice, Virtualized Infrastructure and Cloud Services Management, Cloud Computing: Technologies and Applications Programming, The Basics of Cloud Computing) for major publishers such as Morgan Kaufmann, ELSEVIER and Wiley. He serves as a review board member for a large number of international journals (TC, TPDS, TNSM, TASE, JPDC, Supercomputing, CCPE, FGCS, CJ, and IJPP). He is a professional member of ACM and IEEE computer society. Locally, he serves as U.S. NSF XSEDE (Extreme Science and Engineering Discovery Environment) Campus Champion for Arkansas State University.

Albert Y. Zomaya is the Chair Professor of High Performance Computing & Networking in the School of Information Technologies, University of Sydney, and he also serves as the Director of the Centre for Distributed and High Performance Computing. Professor Zomaya published more than 600 scientific papers and articles and is author, co-author or editor of more than 20 books. He is the Founding Editor in Chief of the IEEE Transactions on Sustainable Computing and serves as an associate editor for more than 20 leading journals. Professor Zomaya served as an Editor in Chief for the IEEE Transactions on Computers (2011-2014).

Professor Zomaya is the recipient of the IEEE Technical Committee on Parallel Processing Outstanding Service Award (2011), the IEEE Technical Committee on Scalable Computing Medal for Excellence in Scalable Computing (2011), and the IEEE Computer Society Technical Achievement Award (2014). He is a Chartered Engineer, a Fellow of AAAS, IEEE, and IET. Professor Zomaya’s research interests are in the areas of parallel and distributed computing and complex systems.

About the Series

Chapman & Hall/CRC Big Data Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM021030
COMPUTERS / Database Management / Data Mining
COM037000
COMPUTERS / Machine Theory