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TrieDF: Efficient In-memory Indexing for Metadata-augmented RDF

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Olivier Pelgrin
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  • PersonId : 1121105
Luis Galárraga
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  • PersonId : 1121104
Katja Hose
  • Function : Author
  • PersonId : 1121106

Abstract

Metadata, such as provenance, versioning, temporal annotations, etc., is vital for the maintenance of RDF data. Despite its importance in the RDF ecosystem, support for metadata-augmented RDF remains limited. Some solutions focus on particular annotation types but no approach so far implements arbitrary levels of metadata in an application-agnostic way. We take a step to tackle this limitation and propose an in-memory tuple store architecture that can handle RDF data augmented with any type of metadata. Our approach, called TrieDF, builds upon the notion of tries to store the indexes and the dictionary of a metadataaugmented RDF dataset. Our experimental evaluation on three use cases shows that TrieDF outperforms state-of-the-art in-memory solutions for RDF in terms of main memory usage and retrieval time, while remaining application-agnostic.
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Dates and versions

hal-03500647 , version 1 (22-12-2021)

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  • HAL Id : hal-03500647 , version 1

Cite

Olivier Pelgrin, Luis Galárraga, Katja Hose. TrieDF: Efficient In-memory Indexing for Metadata-augmented RDF. CEUR-WS.org 2021 - CEUR Workshop Proceedings, Oct 2021, Virtual Event, France. pp.1-10. ⟨hal-03500647⟩
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