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- 3323878.3325802 title "Parallel RDF generation from heterogeneous big data".
- 3323878.3325802 isPartOf proceedings_of_the_international_workshop_on_semantic_big_data.
- 3323878.3325802 name "Parallel RDF generation from heterogeneous big data".
- 3323878.3325802 label "Parallel RDF generation from heterogeneous big data".
- 3323878.3325802 name "Parallel RDF generation from heterogeneous big data".
- 3323878.3325802 topic RML.
- 3323878.3325802 topic constraints.
- 3323878.3325802 topic RDF.
- 3323878.3325802 subject RML.
- 3323878.3325802 subject constraints.
- 3323878.3325802 subject RDF.
- 3323878.3325802 authorList b0_b2139.
- 3323878.3325802 topic RML.
- 3323878.3325802 topic constraints.
- 3323878.3325802 topic RDF.
- 3323878.3325802 abstract "To unlock the value of increasingly available data in high volumes, we need flexible ways to integrate data across different sources. While semantic integration can be provided through RDF generation, current generators insufficiently scale in terms of volume. Generators are limited by memory constraints. Therefore, we developed the RMLStreamer, a generator that parallelizes the ingestion and mapping tasks of RDF generation across multiple instances. In this paper, we analyze what aspects are parallelizable and we introduce an approach for parallel RDF generation. We describe how we implemented our proposed approach, in the frame of the RMLStreamer, and how the resulting scaling behavior compares to other RDF generators. The RMLStreamer ingests data at 50% faster rate than existing generators through parallel ingestion.".
- 3323878.3325802 datePublished "2019".
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