ETL Best Practices for Data Quality Checks in RIS Databases
Autor: | Mohammad Abuosba, Otmane Azeroual, Gunter Saake |
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Rok vydání: | 2019 |
Předmět: |
Computer Networks and Communications
Computer science Process (engineering) Data transformation Context (language use) 02 engineering and technology computer.software_genre data transformation extraction transformation load (ETL) technology 020204 information systems 0502 economics and business 0202 electrical engineering electronic engineering information engineering data quality data integration research information systems (RIS) heterogeneous information sources lcsh:T58.5-58.64 Database lcsh:Information technology Communication metadata 05 social sciences Information quality Data structure Human-Computer Interaction Metadata Data quality 050211 marketing computer Data integration |
Zdroj: | Informatics, Vol 6, Iss 1, p 10 (2019) Informatics Volume 6 Issue 1 |
ISSN: | 2227-9709 |
DOI: | 10.3390/informatics6010010 |
Popis: | The topic of data integration from external data sources or independent IT-systems has received increasing attention recently in IT departments as well as at management level, in particular concerning data integration in federated database systems. An example of the latter are commercial research information systems (RIS), which regularly import, cleanse, transform and prepare the analysis research information of the institutions of a variety of databases. In addition, all these so-called steps must be provided in a secured quality. As several internal and external data sources are loaded for integration into the RIS, ensuring information quality is becoming increasingly challenging for the research institutions. Before the research information is transferred to a RIS, it must be checked and cleaned up. An important factor for successful or competent data integration is therefore always the data quality. The removal of data errors (such as duplicates and harmonization of the data structure, inconsistent data and outdated data, etc.) are essential tasks of data integration using extract, transform, and load (ETL) processes. Data is extracted from the source systems, transformed and loaded into the RIS. At this point conflicts between different data sources are controlled and solved, as well as data quality issues during data integration are eliminated. Against this background, our paper presents the process of data transformation in the context of RIS which gains an overview of the quality of research information in an institution&rsquo s internal and external data sources during its integration into RIS. In addition, the question of how to control and improve the quality issues during the integration process in RIS will be addressed. |
Databáze: | OpenAIRE |
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