ARDI: Automatic Generation of RDFS Models from Heterogeneous Data Sources

Shumet Tadesse Nigatu, Cristina Gomez, Oscar Romero, Katja Hose, Kashif Rabbani

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Abstrakt

The current wealth of information, typically known as Big Data, generates a large amount of available data for organisations. Data Integration provides foundations to query disparate data sources as if they were integrated into a single source. However, current data integration tools are far from being useful for most organisations due to the heterogeneous nature of data sources, which represents a challenge for current frameworks. To enable data integration of highly heterogeneous and disparate data sources, this paper proposes a method to extract the schema from semi-structured (such as JSON and XML) and structured (such as relational) data sources, and generate an equivalent RDFS representation. The output of our method complements current frameworks and reduces the manual workload required to represent the input data sources in terms of the integration canonical data model. Our approach consists of production rules at the meta-model level that guarantee the correctness of the model translations. Finally, a tool for implementing our approach has been developed.
OriginalsprogEngelsk
Titel2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC)
Antal sider7
ForlagIEEE Press
Publikationsdato2019
Sider190-196
Artikelnummer8945018
ISBN (Elektronisk)978-1-7281-2702-6
DOI
StatusUdgivet - 2019
Begivenhed23rd IEEE International EDOC Conference - The Enterprise Computing Conference - Paris, Frankrig
Varighed: 28 okt. 201931 okt. 2019
https://edoc2019.sciencesconf.org/

Konference

Konference23rd IEEE International EDOC Conference - The Enterprise Computing Conference
LandFrankrig
ByParis
Periode28/10/201931/10/2019
Internetadresse
NavnIEEE International Enterprise Distributed Object Computing Conference (EDOC)
ISSN2325-6362

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