What question did this study set out to answer?

The aim is to develop a robust archival system for large RDF datasets that supports full SPARQL querying and improves ingestion speeds.

April 5, 2026Semantic WebOpen Access

Expressive Querying and Scalable Management of Large RDF Archives

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The aim is to develop a robust archival system for large RDF datasets that supports full SPARQL querying and improves ingestion speeds.
Proposed a hybrid storage approach combining aggregated changesets, snapshots, and delta chains.
Interfaced with a modified SPARQL query engine for enhanced querying capabilities.
Evaluated performance using different snapshot creation strategies on the BEAR benchmark.
Achieved up to ten times faster ingestion speed compared to state-of-the-art methods.
Maintained competitive query processing performance with full SPARQL support.
Demonstrated efficient handling of RDF archives with complex querying requirements.

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Implication

This article demonstrates a hybrid storage system improving querying and ingestion performance in RDF archives, suggesting new solutions for data management.

Cite This Study

Pelgrin et al. (2026) studied this question.

synapsesocial.com/papers/69d1fd29a79560c99a0a2ffb https://doi.org/10.1177/22104968261431405

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Implication

This article demonstrates a hybrid storage system improving querying and ingestion performance in RDF archives, suggesting new solutions for data management.

Expressive Querying and Scalable Management of Large RDF Archives

Key Points

Discussion

Implication

Cite This Study

Expressive Querying and Scalable Management of Large RDF Archives

Key Points

Discussion

Implication

Cite This Study