Nomadic Computing for Big Data Analytics.

Analyzing the massive datasets of today's applications will require scalable and sophisticated machine-learning methods. NOMAD, a novel nomadic framework, combines two common approaches: stochastic optimization and distributed computing.

Bibliographic Details
Published in:Computer (00189162) Vol. 49; no. 4; pp. 52 - 61
Main Authors: Yu, Hsiang-Fu, Hsieh, Cho-Jui, Yun, Hyokun, Vishwanathan, S.V.N., Dhillon, Inderjit
Format: Article
Published: IEEE Apr2016
Subjects:
Online Access:View this record in EBSCOhost
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      vid: 49
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        10.1109/MC.2016.116
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        atl: Nomadic Computing for Big Data Analytics.
      aug:
        au:
          Yu, Hsiang-Fu
          Hsieh, Cho-Jui
          Yun, Hyokun
          Vishwanathan, S.V.N.
          Dhillon, Inderjit
        affil:
          University of Texas at Austin
          University of California, Davis
          Amazon.com
          University of California, Santa Cruz
      su:
        Big data
        Computer systems
        Stochastic approximation
        Distributed computing
        Data analytics
      sug:
        subj:
          Big data
          Computer systems
          Stochastic approximation
          Distributed computing
          Data analytics
      keyword:
        big data
        Charge coupled devices
        computing
        distributed algorithms
        Inference algorithms
        Motion pictures
        nomadic algorithms
        Partitioning algorithms
        Scalability
        Stochastic processes
        Web analytics
      ab: Analyzing the massive datasets of today's applications will require scalable and sophisticated machine-learning methods. NOMAD, a novel nomadic framework, combines two common approaches: stochastic optimization and distributed computing.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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