Towards Network Reduction on Big Data

被引:0
作者
Fang, Xing [1 ]
Zhan, Justin [2 ]
Koceja, Nicholas [2 ]
机构
[1] North Carolina Agr & Technol State Univ, Dept Computat Sci & Engn, Greensboro, NC 27411 USA
[2] North Carolina Agr & Technol State Univ, Dept Comp Sci, Greensboro, NC 27411 USA
来源
2013 ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM) | 2013年
基金
美国国家科学基金会;
关键词
Big Data; Categorical Data; Similarity; Algorithm; CLIQUES;
D O I
10.1109/SocialCom.2013.103
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The increasing ease of data collection experience and the increasing availability of large data storage space lead to the existence of very large datasets that are commonly referred as "Big Data". Such data not only take over large amount of database storage, but also increase the difficulties for data analysis due to data diversity, which, also makes the datasets seemingly isolated with each other. In this paper, we present a solution to the problem that is to build up connections among the diverse datasets, based upon their similarities. Particularly, a concept of similarity graph along with a similarity graph generation algorithm were introduced. We then proposed a similarity graph reduction algorithm that reduces vertices of the graph for the purpose of graph simplification.
引用
收藏
页码:685 / 690
页数:6
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