Data placement strategies that speed-up distributed graph query processing-Reference-Cited by-同舟云学术

Data placement strategies that speed-up distributed graph query processing

Published:2020-06-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of The International Workshop on Semantic Big Data
language:
Short-container-title:

Author:

Janke Daniel¹,Staab Steffen²,Leinberger Martin¹

Affiliation:

1. Universität Koblenz-Landau, Germany

2. Universität Stuttgart, Germany and University of Southampton, UK

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3391274.3393633

Reference19 articles.

1. Akhter et al. 2018. An Empirical Evaluation of RDF Graph Partitioning Techniques. In Knowledge Engineering and Knowledge Management. Springer 3--18. Akhter et al. 2018. An Empirical Evaluation of RDF Graph Partitioning Techniques. In Knowledge Engineering and Knowledge Management. Springer 3--18.

2. Li Ding et al. 2005. Tracking RDF Graph Provenance using RDF Molecules. Technical Report. UMBC. https://ebiquity.umbc.edu/paper/html/id/240/Tracking-RDFGraph- Provenance-using-RDF-Molecules Li Ding et al. 2005. Tracking RDF Graph Provenance using RDF Molecules. Technical Report. UMBC. https://ebiquity.umbc.edu/paper/html/id/240/Tracking-RDFGraph- Provenance-using-RDF-Molecules

3. Nikolas Everett. 2015. Wikidata Query Backend Update (take two!). https://lists.wikimedia.org/pipermail/wikidata-tech/2015-March/000740.html. Nikolas Everett. 2015. Wikidata Query Backend Update (take two!). https://lists.wikimedia.org/pipermail/wikidata-tech/2015-March/000740.html.

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