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(Conceptual thing) Extract Transform and Load Metamodel
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Approach for mapping between different models of DSpace and VIVO
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- This approach consists in producing a VIVO representation of DSpace data from a source data extraction mechanism directly from the Postgres database used by DSpace.
- The block 1 presents the semantization process of the Dspace data structure. The process consists in extracting the database schema and storing it in an XML or CSV file. The translation of this XML file into RDF is produced by the XML2JSON and JSON2RDF utilities. The result of this process is an RDF ontology representing the structure of the source database tables. This process does not involve any particular risk since we have implemented this type of processing in many of our projects.
- Block 2 which has been processed similarly to block one is mainly oriented to extract the data contained in the database. The result is therefore a graph of data in RDF format that are structured in the Dspace perspective.
- Finally, block 3 consists in performing a mapping between the semantized data from the Dspace perspective to a semantized data from the VIVO perspective. As for the previous approach, this mapping is performed by a SPARQL construct query.
- The process of the third approach is similar to the second approach except that the data source comes from the Dspace REST-API instead of from the Postgres database.
Third approach: Using Model Driven Development based on OpenApi/Swagger specification
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Software communication between VIVO and DSpace
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