Big Data Algorithms, Analytics, and Applications

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.

Scalable Indexing for Big Data Processing; Hisham Mohamed and Stephane Marchand-MailletScalability and Cost Evaluation of Incremental Data Processing using Amazon's Hadoop Service; Xing Wu, Yan Liu, and Ian GortonSingular Value Decomposition, Clustering, and Indexing for Similarity Search for Large Data Sets in High-Dimensional Spaces; Alexander ThomasianMultiple Sequence Alignment and Clustering with Dot Matrices, Entropy, and Genetic Algorithms; John TsiligaridisApproaches 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 GhazawiThe Art of Scheduling for Big Data Science; Florin Pop and Valentin CristeaTime-Space Scheduling in the MapReduce Framework; Zhuo Tang, Lingang Jiang, Ling Qi, Kenli Li, and Keqin LiThe 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 FeoKSC-net: Community Detection for Big Data Networks; Raghvendra Mall and Johan A.K. SuykensMaking Big Data Transparent to the Software Developers' Community; Yu Wu, Jessica Kropczynski, and John M. CarrollKey Technologies for Big Data Stream Computing; Dawei Sun, Guangyan Zhang, Weimin Zheng, and Keqin LiStreaming Algorithms for Big Data Processing on Multicore Architecture; Marat ZhanikeevOrganic Streams: A Unified Framework for Personal Big Data Integration and Organization Towards Social Sharing and Individualized Sustainable Use; Xiaokang Zhou and Qun JinManaging Big Trajectory Data: Online Processing of Positional Streams; Kostas Patroumpas and Timos SellisPersonal Data Protection Aspects of Big Data; Paolo BalboniPrivacy-Preserving Big Data Management: The Case of OLAP; Alfredo CuzzocreaBig Data in Finance; Taruna Seth and Vipin ChaudharySemantic-Based Heterogeneous Multimedia Big Data Retrieval; Kehua Guo and Jianhua MaTopic Modeling for Large-Scale Multimedia Analysis and Retrieval; Juan Hu, Yi Fang, Nam Ling, and Li SongBig Data Biometrics Processing: A Case Study of an Iris Matching Algorithm on Intel Xeon Phi; Xueyan Li and Chen LiuStoring, Managing, and Analyzing Big Satellite Data: Experiences and Lessons Learned from a Real-World Application; Ziliang ZongBarriers to the Adoption of Big-Data Applications in the Social Sector; Elena Strange

Kuan-Ching Li is a professor in the Department of Computer Science and Information Engineering at Providence University, Taiwan. He was department chair in 2009, has been special assistant to the university president since 2010, and was appointed vice dean for the Office of International and Cross-Strait Affairs (OIA) in 2014. He earned a PhD in 2001 from the University of São Paulo, Brazil. Dr. Li is a recipient of awards from NVIDIA, the Ministry of Education (MOE)/Taiwan, and the Ministry of Science and Technology (MOST)/Taiwan. He also received guest professorships at universities in China, including Xiamen University (XMU), Huazhong University of Science and Technology (HUST), Lanzhou University (LZU), Shanghai University (SHU), Anhui University of Science and Technology (AUST), and Lanzhou Jiaotong University (LZJTU). He has been involved actively in conferences and workshops as a program/general/steering conference chairman and in numerous conferences and workshops as a program committee member, and he has organized numerous conferences related to high-performance computing and computational science and engineering.Dr. Li is the editor in chief of the 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 Interscience. He also serves on a number of journals’ editorial boards and guest editorships. In addition, he has been acting as editor/coeditor of several technical professional books, published by CRC Press and IGI Global. His topics of interest include networked computing, GPU computing, parallel software design, and performance evaluation and benchmarking. Dr. Li is a member of the Taiwan Association of Cloud Computing (TACC), a senior member of the IEEE, and a fellow of the IET. Hai Jiang is an associate professor in the Department of Computer Science at Arkansas State University, United States. He earned a BS at Beijing University of Posts and Telecommunications, China, and MA and PhD degrees at Wayne State University. His research interests include parallel and distributed systems, computer and network security, high-performance computing and communication, big data, and modeling and simulation.Dr. Jiang has published one book and several research papers in major international journals and conference proceedings. He has served as a US National Science Foundation proposal review panelist and a US Department of Energy (DoE) Smart Grid Investment Grant (SGIG) reviewer multiple times. He serves as an editor for the International Journal of High Performance Computing and Networking (IJHPCN); a regional editor for the International Journal of Computational Science and Engineering (IJCSE) as well as the International Journal of Embedded Systems (IJES); an editorial board member for the International Journal of Big Data Intelligence (IJBDI), the Scientific World Journal (TSWJ), the Open Journal of Internet of Things (OJIOT), and the GSTF Journal on Social Computing (JSC); and a guest editor for the 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, ScalCom, 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 publishers such as Morgan Kaufmann, Elsevier, and Wiley. Dr. Jiang 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 the IEEE Computer Society. Locally, he serves as US NSF XSEDE (Extreme Science and Engineering Discovery Environment) Campus Champion for Arkansas State University. Dr. Laurence T. Yang is a professor in the Department of Computer Science at St. Francis Xavier University, Canada. His research includes parallel and distributed computing, embedded and ubiquitous/pervasive computing, cyber–physical–social systems, and big data. Dr. Yang has published 200+ refereed international journal papers in the above areas; about one-third are in IEEE/ACM transactions/journals and the rest mostly are in Elsevier, Springer, and Wiley journals. He has been involved in conferences and workshops as a program/ general/steering conference chai