A Semantically-Based Big Data Processing System Using Hadoop and Map-Reduce

被引：0
|
作者：
Wang Wanting [1 ]
Qin Zheng [1 ,2 ]
机构：
[1] Shanghai Univ Finance & Econ, Sch Informat Management & Engn, Shanghai, Peoples R China
[2] South Univ Sci & Technol China, Shenzhen, Peoples R China
来源：
SOCIALLY AWARE ORGANISATIONS AND TECHNOLOGIES: IMPACT AND CHALLENGES | 2016年 / 477卷
关键词：
Big data; Semantics; Data integration; Distributed computation;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In financial industry, a wide range of financial systems generate vast amount of data in different structures, which change with compliance rules change and hard to manage due to their heterogeneity. This paper introduces a semantically-based big data processing system to integrate the data from different sources, which realizes the query and computation in semantic layer. The system provides a new data management way for the financial industry. With Semantic Web, the information can be managed, integrated, and collaborated in a more fluent way than it in traditional ETL. In order to clear the complex logical relationship among data, the system uses SPARQL to query. Through Map-Reduce, this system, based on Hadoop and Hbase can improve the processing speed for big data.
引用
收藏
页码：246 / 247
页数：2
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