Hierarchical MapReduce: towards simplified cross-domain data processing

Author:

Luo Yuan1,Plale Beth1,Guo Zhenhua1,Li Wilfred W.2,Qiu Judy1,Sun Yiming1

Affiliation:

1. School of Informatics and Computing; Indiana University; Bloomington IN, 47405 USA

2. San Diego Supercomputer Center; University of California; San Diego La Jolla, CA, 92093 USA

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Reference35 articles.

1. Mapreduce: simplified data processing on large clusters;Dean;Communications of the ACM,2008

2. FutureGrid http://www.futuregrid.org

3. XSEDE https://xsede.org/

4. WORKS ’09;Wang,2009

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1. Hierarchical Spark: A Multi-Cluster Big Data Computing Framework;2017 IEEE 10th International Conference on Cloud Computing (CLOUD);2017-06

2. Introducing distributed dynamic data-intensive (D3) science: Understanding applications and infrastructure;Concurrency and Computation: Practice and Experience;2017-02-02

3. FedLoop: Looping on Federated MapReduce;2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications;2014-09

4. Special Issue for Emerging Computational Methods for the Life Sciences Workshop;Concurrency and Computation: Practice and Experience;2013-02-04

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