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With the growing use of graph-based applications across various domains, the demand for efficient and scalable RDF (Resource Description Framework) systems has intensified. As organizations increasingly deploy RDF systems on cloud infrastructures, understanding the trade-offs between performance and cost becomes critical. This paper presents a comprehensive experimental comparison of multiple RDF systems, analyzing their performance in cloud and on-premises environments. Our study evaluates key metrics such as query execution time, data ingestion speed, storage efficiency, and cost-effectiveness across several benchmarks, including LDBC SNB, WatDiv, LUBM, and DBpedia. The experiments reveal distinct advantages and limitations in RDF systems. Notably, Virtuoso demonstrated strong performance and cost-efficiency across most datasets, while systems like gStore exhibited higher variability under stress conditions. Our findings offer actionable insights for practitioners seeking to balance performance and cost when deploying RDF systems in cloud environments. We also highlight areas for improvement in system documentation, compatibility, and stability under extreme workloads. Future work will explore alternative architectures and further optimization strategies for cloud-based RDF solutions. The related code, scripts and data of this paper are available online ( https://github.com/unswdb/RDF_on_Cloud ).

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An Experimental Comparison of RDF Systems on Cloud

  • 会议论文

  • 发表日期：

    2025年

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  • Yi Ding,
  • Hualong Lin,
  • Zhengyi Yang,
  • Dong Wen,
  • Xiaoyang Wang,
  • Wenjie Zhang

摘要

With the growing use of graph-based applications across various domains, the demand for efficient and scalable RDF (Resource Description Framework) systems has intensified. As organizations increasingly deploy RDF systems on cloud infrastructures, understanding the trade-offs between performance and cost becomes critical. This paper presents a comprehensive experimental comparison of multiple RDF systems, analyzing their performance in cloud and on-premises environments. Our study evaluates key metrics such as query execution time, data ingestion speed, storage efficiency, and cost-effectiveness across several benchmarks, including LDBC SNB, WatDiv, LUBM, and DBpedia. The experiments reveal distinct advantages and limitations in RDF systems. Notably, Virtuoso demonstrated strong performance and cost-efficiency across most datasets, while systems like gStore exhibited higher variability under stress conditions. Our findings offer actionable insights for practitioners seeking to balance performance and cost when deploying RDF systems in cloud environments. We also highlight areas for improvement in system documentation, compatibility, and stability under extreme workloads. Future work will explore alternative architectures and further optimization strategies for cloud-based RDF solutions. The related code, scripts and data of this paper are available online ( https://github.com/unswdb/RDF_on_Cloud ).