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Big Data over Networks
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  • Cited by 7
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    This (lowercase (translateProductType product.productType)) has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Jung, Alexander Tran, Nguyen and Mara, Alexandru 2018. When Is Network Lasso Accurate?. Frontiers in Applied Mathematics and Statistics, Vol. 3,

    Jung, Alexander and Hulsebos, Madelon 2018. The Network Nullspace Property for Compressed Sensing of Big Data Over Networks. Frontiers in Applied Mathematics and Statistics, Vol. 4,

    Jung, Alexander Heimowitz, Ayelet and Eldar, Yonina C. 2017. The network nullspace property for compressed sensing over networks. p. 644.

    Berger, Peter Hannak, Gabor and Matz, Gerald 2017. Graph Signal Recovery via Primal-Dual Algorithms for Total Variation Minimization. IEEE Journal of Selected Topics in Signal Processing, Vol. 11, Issue. 6, p. 842.

    Berger, Peter Hannak, Gabor and Matz, Gerald 2017. Coordinate descent accelerations for signal recovery on scale-free graphs based on total variation minimization. p. 1689.

    Jung, Alexander Berger, Peter Hannak, Gabor and Matz, Gerald 2016. Scalable graph signal recovery for big data over networks. p. 1.

    Hannak, Gabor Berger, Peter Matz, Gerald and Jung, Alexander 2016. Efficient graph signal recovery over big networks. p. 1839.


Book description

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.

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