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

Olivier Pelgrin, Luis Galárraga, Katja Hose

Research output: Contribution to journalConference article in JournalResearchpeer-review

1 Citation (Scopus)
37 Downloads (Pure)


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 metadata-augmented 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.

Original languageEnglish
JournalCEUR Workshop Proceedings
Pages (from-to)20-29
Number of pages10
Publication statusPublished - 2021
Event7th Workshop on Managing the Evolution and Preservation of the Data Web, MEPDaW 2021 - Virtual, Online
Duration: 25 Oct 2021 → …


Conference7th Workshop on Managing the Evolution and Preservation of the Data Web, MEPDaW 2021
CityVirtual, Online
Period25/10/2021 → …

Bibliographical note

Funding Information:
Acknowledgements. This research was partially funded by the Danish Council for Independent Research (DFF) under grant agreement no. DFF-8048-00051B and the Poul Due Jensen Foundation.

Publisher Copyright:
© 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)


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