Towards De-duplication Framework in Big Data Analysis. A Case Study

被引：3
|
作者：
Maslankowski, Jacek [1 ]
机构：
[1] Univ Gdansk, Dept Business Informat, Gdansk, Poland
来源：
INFORMATION SYSTEMS: DEVELOPMENT, RESEARCH, APPLICATIONS, EDUCATION | 2016年 / 264卷
关键词：
Business informatics; Big Data; Unstructured data; Data analysis; Data quality; ISSUES;
D O I：
10.1007/978-3-319-46642-2_7
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 analysis gives access to wider perspectives of information. Especially it allows processing unstructured and structured data together. However lots of data sources do not mean that the quality of data is enough to provide reliable results. There are several different quality indicators related to Big Data analysis. In this paper we will focus on two of them that are the most critical in the first phase of data processing: ambiguousness and duplicates. The goal of this paper is to present the proposal of the framework used to eliminate duplicates in large datasets acquired with Big Data analysis.
引用
收藏
页码：104 / 113
页数：10
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