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Frontiers of Computer Science

Front. Comput. Sci.    2017, Vol. 11 Issue (4) : 608-621     DOI: 10.1007/s11704-016-5003-y
RESEARCH ARTICLE |
A parallel computing framework for big data
Guoliang CHEN1,2, Rui MAO1,2(), Kezhong LU1,2
1. Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen 518060, China
2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
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Abstract

Big data has received great attention in research and application. However, most of the current efforts focus on system and application to handle the challenges of “volume” and “velocity”, and not much has been done on the theoretical foundation and to handle the challenge of “variety”. Based on metric-space indexing and computationalcomplexity theory, we propose a parallel computing framework for big data. This framework consists of three components, i.e., universal representation of big data by abstracting various data types into metric space, partitioning of big data based on pair-wise distances in metric space, and parallel computing of big data with the NC-class computing theory.

Keywords NC-computing      metric space      data partitioning      parallel computing     
Corresponding Authors: Rui MAO   
Just Accepted Date: 29 March 2016   Online First Date: 17 March 2017    Issue Date: 26 July 2017
 Cite this article:   
Guoliang CHEN,Rui MAO,Kezhong LU. A parallel computing framework for big data[J]. Front. Comput. Sci., 2017, 11(4): 608-621.
 URL:  
http://journal.hep.com.cn/fcs/EN/10.1007/s11704-016-5003-y
http://journal.hep.com.cn/fcs/EN/Y2017/V11/I4/608
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Guoliang CHEN
Rui MAO
Kezhong LU
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