Data Quality Measures and Data Cleansing for Research Information Systems
Otmane Azeroual, Gunter Saake, Mohammad Abuosba
Journal of Digital Information Management, Vol. 16, No. 1, 2018.
DOI: https://doi.org/10.6025/jdim/2018/16/1/12-21
Link: http://dline.info/fpaper/jdim/v16i1/jdimv16i1_2.pdf
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.