Big Data over NetworksShuguang Cui, Alfred O. Hero, III, Zhi-Quan Luo, José M. F. Moura Utilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data over large systems, making it essential reading for graduate students, scientific researchers and industry practitioners alike. |
Contents
| 3 | |
References | 31 |
Sparsityaware distributed learning | 37 |
Optimization algorithms for big data with application in wireless networks | 66 |
References | 97 |
Big data analytics systems | 137 |
References | 158 |
References | 178 |
References | 242 |
mapping topic networks in Twitter data | 278 |
Acknowledgements | 298 |
validation and uncertainty | 337 |
References | 360 |
Genesetbased inference of biological network topologies from big molecular | 391 |
References | 406 |
populations | 424 |
challenges and opportunities | 180 |
Acknowledgement | 211 |
Big data processing for smart grid security | 217 |
References | 432 |
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Big Data over Networks Shuguang Cui,Alfred O. Hero, III,Zhi-Quan Luo,José M. F. Moura Limited preview - 2016 |
Common terms and phrases
ADMM allocation analysis applications approach approximation Arab Spring backhaul Bayesian Bayesian networks beamforming big data Bioinformatics biological networks block cache clustering co-clustering computing considered constraints convergence convex convex optimization correlation mining correlation network covariance Data Mining data traffic dataset defined denote discussed distributed storage downloading edges estimation factor model Figure framework gene expression gene regulatory networks gene sets graph high-dimensional IEEE IEEE Transactions International Conference iteration Journal linear Machine Learning Markov measures methods minimization network component network inference nuclear norm objective function optimization problem parameters performance players random reconstruction regulatory networks resource samples scalable scheduling scheme Section Signal Processing smart grid social influence social media social networks solution solving sparse sparsity statistical storage node strategy subproblem symbols tasks tensor Theorem threshold topics tweets Twitter update variables vector wireless network