Open Source Data Quality Tools: Revisited

被引：2

作者：

Pulla, Venkata Sai Venkatesh ^{[1
]}

Varol, Cihan ^{[1
]}

Al, Murat ^{[2
]}

机构：

[1] Sam Houston State Univ, Dept Comp Sci, Huntsville, TX 77341 USA

[2] Univ North Florida, Sch Comp, Jacksonville, FL 32224 USA

来源：

INFORMATION TECHNOLOGY: NEW GENERATIONS | 2016年 / 448卷

关键词：

Data cleaning; Data integration; Data profiling; Data quality; Data quality tools;

D O I：

10.1007/978-3-319-32467-8_77

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

High data quality is defined as the reliability and application efficiency of data present in a system. Maintaining high data quality has become a key feature for most organizations. Different data quality tools are used for extracting, cleaning, and matching data sources. In this paper, we first introduce state of the art open source data quality tools, specifically Talend Open Studio, DataCleaner, WinPure, Data Preparator, Data Match, DataMartist, Pentaho Kettle, SQL Power Architect, SQL Power DQguru, and DQ Analyzer. Secondly, we compare these tools based on their key features and performance in data profiling, integration, and cleaning. Overall, DataCleaner scores highest among the considered tools.

引用

页码：893 / 902

页数：10