Data Cleaning Framework: An Extensible Approach to Data Cleaning

Data Cleaning

Gu, Randy S. ; Chang ; Kevin C-C.

关键词: Data Cleaning;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/18304/Gu_Randy.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The growing dependence of society on enormous quantities of information stored electronically has led to a corresponding rise in errors in this information. The stored data can be critically important, necessitating new ways of correcting anomalous records. Current cleaning techniques are very domain-specific and hard to extend, hindering their use in some areas. This work proposes an extensible framework for data cleaning, allowing users to customize the cleaning to their specific requirements. It defines categories of common cleaning operations, allowing more robust support for user-implemented cleaning functions in these categories. The experimental results show that the proposed data cleaning framework is an effective approach to cleaning data for arbitrary domains.