Data Quality Measures and Data Cleansing for Research Information Systems
Author(s)
Issue Date
February 1, 2018
Publisher
Digital Information Research Foundation
Source
O Azeroual, G Saake, M Abuosba (2018), "Data Quality Measures and Data Cleansing for Research Information Systems". Journal of Digital Information Management 16(1): 12-21
Type
Article
Journal
Journal of Digital Information Management
Abstract
The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased.
In this paper, possible measures and the new techniques of data cleansing for improving and increasing the data quality in research information systems will be presented and how these are to be applied to the research information.
Description
10 pages.-- Published in the Journal of Digital Information Management, Vol 16 Iss 1 (Feb 2018)
URI
https://dspacecris.eurocris.org/handle/11366/632
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