Description
Taylor & Francis Big Data Management And Processing by Kuan-Ching Li
From the Foreword:_x000D__x000D__x000D_"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."_x000D__x000D__x000D_---Sartaj Sahni, University of Florida, USA_x000D__x000D__x000D_"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._x000D__x000D__x000D_--Hai Jin, Huazhong University of Science and Technology, China_x000D__x000D__x000D_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._x000D__x000D__x000D__x000D__x000D__x000D_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._x000D__x000D__x000D__x000D__x000D__x000D_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._x000D_ _x000D_
1 Big Data: Legal Compliance and Quality Management _x000D_
Paolo Balboni and Theodora Dragan_x000D_
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2 EnergyManagement for Green Big Data Centers _x000D_
Chonglin Gu, Hejiao Huang, and Xiaohua Jia_x000D_
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3 The Art of In-Memory Computing for Big Data Processing _x000D_
Mihaela-Andreea Vasile and Florin Pop_x000D_
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4 Scheduling Nested Transactions on In-Memory Data Grids _x000D_
Junwhan Kim, Roberto Palmieri, and Binoy Ravindran_x000D_
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5 Co-Scheduling High-Performance Computing Applications_x000D_
Guillaume Aupy, Anne Benoit, Loic Pottier, Padma Raghavan, Yves Robert, and Manu Shantharam_x000D_
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6 ResourceManagement forMapReduce Jobs Performing Big Data Analytics _x000D_
Norman Lim and Shikharesh Majumdar_x000D_
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7 Tyche: An Efficient Ethernet-Based Protocol for Converged Networked Storage _x000D_
Pilar Gonzalez-Ferez and Angelos Bilas_x000D_
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8 Parallel Backpropagation Neural Network for Big Data Processing on Many-Core Platform _x000D_
Boyang Li and Chen Liu_x000D_
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9 SQL-on-Hadoop Systems: State-of-the-Art Exploration, Models, Performances, Issues, and Recommendations _x000D_
Alfredo Cuzzocrea, Rim Moussa, and Soror Sahri_x000D_
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10 One Platform Rules All: From Hadoop 1.0 to Hadoop 2.0 and Spark _x000D_
Xiongpai Qin and Keqin Li_x000D_
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11 Security, Privacy, and Trust for User-Generated Content: The Challenges and Solutions _x000D_
Yuhong Liu, Yu Wang, and Nam Ling_x000D_
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12 Role of Real-Time Big Data Processing in the Internet of Things _x000D_
Miyuru Dayarathna, Paul Fremantle, Srinath Perera, and Sriskandarajah Suhothayan_x000D_
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13 End-to-End Security Framework for Big Sensing Data Streams_x000D_
Deepak Puthal, Surya Nepal, Rajiv Ranjan, and Jinjun Chen_x000D_
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14 Considerations on the Use of Custom Accelerators for Big Data Analytics_x000D_
Vito Giovanni Castellana, Antonino Tumeo, Marco Minutoli, Marco Lattuada, and Fabrizio Ferrandi_x000D_
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15 Complex Mining from Uncertain Big Data in Distributed Environments: Problems, Definitions, and Two Effective and Efficient Algorithms _x000D_
Alfredo Cuzzocrea, Carson Kai-Sang Leung, Fan Jiang, and Richard Kyle MacKinnon_x000D_
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16 Clustering in Big Data _x000D_
Min Chen, Simone A. Ludwig, and Keqin Li_x000D_
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17 Large Graph Computing Systems_x000D_
Chengwen Wu, Guangyan Zhang, Keqin Li, and Weimin Zheng_x000D_
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18 Big Data in Genomics_x000D_
Huaming Chen, Jiangning Song, Jun Shen, and Lei Wang_x000D_
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19 Maximizing the Return on Investment in Big Data Projects: An Approach Based upon the Incremental Funding of Project Development_x000D_
Antonio Juarez Alencar, Mauro Penha Bastos, Eber Assis Schmitz, Monica Ferreira da Silva, and Petros Sotirios Stefaneas_x000D_
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20 Parallel DataMining and Applications in Hospital Big Data Processing_x000D_
Jianguo Chen, Zhuo Tang, Kenli Li, and Keqin Li_x000D_
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21 Big Data in the Parking Lot_x000D_
Ryan Florin, Syedmeysam Abolghasemi, Aida Ghazi Zadeh, and Stephan Olariu_x000D_