A parallel computing framework for big data

被引：4

作者：

Chen, Guoliang ^{[1
,2
]}

Mao, Rui ^{[1
,2
]}

Lu, Kezhong ^{[1
,2
]}

机构：

[1] Guangdong Prov Key Lab Popular High Performance C, Shenzhen 518060, Peoples R China

[2] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China

来源：

FRONTIERS OF COMPUTER SCIENCE | 2017年 / 11卷 / 04期

基金：

国家高技术研究发展计划(863计划);

关键词：

NC-computing; metric space; data partitioning; parallel computing; SIMILARITY SEARCH; METRIC-SPACES; QUERIES;

D O I：

10.1007/s11704-016-5003-y

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：608 / 621

页数：14

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