Chapman and Hall/CRC
498 pages | 155 B/W Illus.
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:
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.
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
Scalable Indexing for Big Data Processing; Hisham Mohamed and Stephane Marchand-Maillet
Scalability and Cost Evaluation of Incremental Data Processing using Amazon's Hadoop Service; Xing Wu, Yan Liu, and Ian Gorton
Singular Value Decomposition, Clustering, and Indexing for Similarity Search for Large Data Sets in High-Dimensional Spaces; Alexander Thomasian
Multiple Sequence Alignment and Clustering with Dot Matrices, Entropy, and Genetic Algorithms; John Tsiligaridis
Approaches for High-Performance Big Data Processing: Applications and Challenges; Ouidad Achahbar, Mohamed Riduan Abid, Mohamed Bakhouya, Chaker El Amrani, Jaafar Gaber, Mohammed Essaaidi, and Tarek A. El Ghazawi
The Art of Scheduling for Big Data Science; Florin Pop and Valentin Cristea
Time-Space Scheduling in the MapReduce Framework; Zhuo Tang, Lingang Jiang, Ling Qi, Kenli Li, and Keqin Li
The Graph Engine for Multithreaded Systems Graph Database System for Commodity Clusters; Alessandro Morari, Vito Giovanni Caltellana, Oreste Villa, Jesse Weaver, Greg Williams, David Haglin, Antonino Tumeo, and John Feo
KSC-net: Community Detection for Big Data Networks; Raghvendra Mall and Johan A.K. Suykens
Making Big Data Transparent to the Software Developers' Community; Yu Wu, Jessica Kropczynski, and John M. Carroll
Key Technologies for Big Data Stream Computing; Dawei Sun, Guangyan Zhang, Weimin Zheng, and Keqin Li
Streaming Algorithms for Big Data Processing on Multicore Architecture; Marat Zhanikeev
Organic Streams: A Unified Framework for Personal Big Data Integration and Organization Towards Social Sharing and Individualized Sustainable Use; Xiaokang Zhou and Qun Jin
Managing Big Trajectory Data: Online Processing of Positional Streams; Kostas Patroumpas and Timos Sellis
Personal Data Protection Aspects of Big Data; Paolo Balboni
Privacy-Preserving Big Data Management: The Case of OLAP; Alfredo Cuzzocrea
Big Data in Finance; Taruna Seth and Vipin Chaudhary
Semantic-Based Heterogeneous Multimedia Big Data Retrieval; Kehua Guo and Jianhua Ma
Topic Modeling for Large-Scale Multimedia Analysis and Retrieval; Juan Hu, Yi Fang, Nam Ling, and Li Song
Big Data Biometrics Processing: A Case Study of an Iris Matching Algorithm on Intel Xeon Phi; Xueyan Li and Chen Liu
Storing, Managing, and Analyzing Big Satellite Data: Experiences and Lessons Learned from a Real-World Application; Ziliang Zong
Barriers to the Adoption of Big-Data Applications in the Social Sector; Elena Strange