Big Data : Principles and Paradigms

__Big Data: Principles and Paradigms__ captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues. * Covers computational platforms supporting Big Data applications * Addresses key principles underlying Big Data computing * Examines key developments supporting next generation Big Data platforms * Explores the challenges in Big Data computing and ways to overcome them * Contains expert contributors from both academia and industry Content: Front Matter,Copyright,List of contributors,About the Editors,Preface,AcknowledgmentsEntitled to full textPart I: Big Data ScienceChapter 1 - Big Data Analytics = Machine Learning + Cloud Computing, Pages 3-38, C. Wu, R. Buyya, K. Ramamohanarao Chapter 2 - Real-Time Analytics, Pages 39-61, Z. Milosevic, W. Chen, A. Berry, F.A. Rabhi Chapter 3 - Big Data Analytics for Social Media, Pages 63-94, S. Kannan, S. Karuppusamy, A. Nedunchezhian, P. Venkateshan, P. Wang, N. Bojja, A. Kejariwal Chapter 4 - Deep Learning and Its Parallelization, Pages 95-118, X. Li, G. Zhang, K. Li, W. Zheng Chapter 5 - Characterization and Traversal of Large Real-World Networks, Pages 119-136, A. Garcia-Robledo, A. Diaz-Perez, G. Morales-Luna Chapter 6 - Database Techniques for Big Data, Pages 139-159, P. Ameri Chapter 7 - Resource Management in Big Data Processing Systems, Pages 161-188, S. Tang, B. He, H. Liu, B.-S. Lee Chapter 8 - Local Resource Consumption Shaping: A Case for MapReduce, Pages 189-214, P. Lu, Y.C. Lee, T. Ryan, V. Gramoli, A.Y. Zomaya Chapter 9 - System Optimization for Big Data Processing, Pages 215-238, R. Li, X. Dong, X. Gu, Z. Xue, K. Li Chapter 10 - Packing Algorithms for Big Data Replay on Multicore, Pages 239-266, M. Zhanikeev Chapter 11 - Spatial Privacy Challenges in Social Networks, Pages 269-283, R.O. Sinnott, S. Sun Chapter 12 - Security and Privacy in Big Data, Pages 285-308, L. Ou, Z. Qin, H. Yin, K. Li Chapter 13 - Location Inferring in Internet of Things and Big Data, Pages 309-335, W. Xi, J. Han, K. Li, Z. Jiang, H. Ding Chapter 14 - A Framework for Mining Thai Public Opinions, Pages 339-355, C. Deerosejanadej, S. Prom-on, T. Achalakul Chapter 15 - A Case Study in Big Data Analytics: Exploring Twitter Sentiment Analysis and the Weather, Pages 357-388, R.O. Sinnott, H. Duan, Y. Sun Chapter 16 - Dynamic Uncertainty-Based Analytics for Caching Performance Improvements in Mobile Broadband Wireless Networks, Pages 389-415, S. Dutta, A. Narang Chapter 17 - Big Data Analytics on a Smart Grid: Mining PMU Data for Event and Anomaly Detection, Pages 417-429, S. Wallace, X. Zhao, D. Nguyen, K.-T. Lu Chapter 18 - eScience and Big Data Workflows in Clouds: A Taxonomy and Survey, Pages 431-455, A.C. Zhou, B. He, S. Ibrahim Index, Pages 457-468

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications.

To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.

• Covers computational platforms supporting Big Data applications
• Addresses key principles underlying Big Data computing
• Examines key developments supporting next generation Big Data platforms
• Explores the challenges in Big Data computing and ways to overcome them
• Contains expert contributors from both academia and industry