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Loading data into an RDF store

A benchmark task measuring the time taken and resources used by RDF stores when loading flat RDF data (triples or quads).

Methodology

Data

Flat distributions of any dataset in the flat category of RiverBench may be used for this task.

Workload

In this task, an RDF store is set up (for example, Apache Jena TDB2 or Virtuoso) and then instructed to load a flat file containing RDF statements (triples or quads).

  • When comparing multiple RDF stores, identical input data (serialized in the same format) should be used for all stores.
  • The benchmark includes the time taken to deserialize the input data and insert the resulting RDF statements into the store, considering the entire process and the impact of the underlying I/O.
  • Input data may be either batched or streamed, depending on the capabilities of the RDF store being tested and the specific research questions being addressed.
    • When using batched input, the input dataset is split into chunks consisting of N statements, and each chunk is processed by the system separately. The metrics (see below) are typically calculated per chunk.
    • When using streamed input, the input dataset is processed as a continuous stream of individual statements (in RDF-STaX terminology: flat RDF stream). The metrics are typically calculated in regular time intervals or after processing a certain number of statements.

Metrics

  • Time taken to deserialize the input data and insert the resulting RDF statements into the RDF store. From this measurement, the insertion throughput (in statements per second) can be calculated.
  • Memory usage during and after the loading process.
  • Storage space used by the RDF store during and after the loading process.
  • Total CPU time used during the loading process.

Results

There are no results with RiverBench available for this task yet.

Examples and references

  • Such a benchmark was performed in a paper comparing several RDF stores on IoT devices (Section 8). There, the authors measured the time taken to load the data and the memory usage.
    • Le-Tuan, A., Hayes, C., Hauswirth, M., & Le-Phuoc, D. (2020). Pushing the Scalability of RDF Engines on IoT Edge Devices. Sensors, 20(10), 2788. https://doi.org/10.3390/s20102788

Metadata

Info

Download this metadata in RDF: Turtle, N-Triples, RDF/XML, Jelly

General information