Data integration is a digital technology used to combine data from multiple sources and provide users with a unified view of data assets (Chen, Hu, & Xu, 2015; Davidovski, 2018). Data is typically loaded into a data warehouse via Execute-Transform-Load (ETL) operations (Hose et al. 2015); sometimes the transformed data lacks data integrity. This paper explores best practices in using semantic transformation to address data integrity issues caused by data integration
Abstract. Data integration problems are commonly viewed as inter-operability issues, where the burde...
Data interoperability encompasses the many data management activities needed for effective informati...
Semantic reconciliation is an important step in determining logical connectivity between a data sour...
Integration of data sources opens up possibilities for new and valuable applications of data that ca...
Integration of data sources opens up possibilities for new and valuable applications of data that ca...
Transforming data from different information systems is important and challenging for inte-gration p...
The topic of data integration from external data sources or independent IT-systems has received incr...
abstract: Current tools that facilitate the extract-transform-load (ETL) process focus on ETL workfl...
Data integration is a critical problem in our increasingly interconnected but inevitably heterogeneo...
Semantic reconciliation is an important step in determining logical connectivity between a data sour...
When developing data transformations—a task omnipresent in applications like data integration, data ...
In modern enterprises, both operational and organizational data is typically spread across multiple ...
Dealing with inconsistencies is one the main challenges in data integration systems, where data stor...
Title: Towards Trustworthy Linked Data Integration and Consumption Author: RNDr. Tomáš Knap Departme...
data integration systems, in the case where the global schema contains the classical key and foreign...
Abstract. Data integration problems are commonly viewed as inter-operability issues, where the burde...
Data interoperability encompasses the many data management activities needed for effective informati...
Semantic reconciliation is an important step in determining logical connectivity between a data sour...
Integration of data sources opens up possibilities for new and valuable applications of data that ca...
Integration of data sources opens up possibilities for new and valuable applications of data that ca...
Transforming data from different information systems is important and challenging for inte-gration p...
The topic of data integration from external data sources or independent IT-systems has received incr...
abstract: Current tools that facilitate the extract-transform-load (ETL) process focus on ETL workfl...
Data integration is a critical problem in our increasingly interconnected but inevitably heterogeneo...
Semantic reconciliation is an important step in determining logical connectivity between a data sour...
When developing data transformations—a task omnipresent in applications like data integration, data ...
In modern enterprises, both operational and organizational data is typically spread across multiple ...
Dealing with inconsistencies is one the main challenges in data integration systems, where data stor...
Title: Towards Trustworthy Linked Data Integration and Consumption Author: RNDr. Tomáš Knap Departme...
data integration systems, in the case where the global schema contains the classical key and foreign...
Abstract. Data integration problems are commonly viewed as inter-operability issues, where the burde...
Data interoperability encompasses the many data management activities needed for effective informati...
Semantic reconciliation is an important step in determining logical connectivity between a data sour...