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Task: Flat RDF compression (development version)

Task identifier: flat-compression

A benchmark task measuring the compression efficiency of flat RDF serializations.

Methodology

Data

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

Workload

The task consists of serializing RDF data to bytes and measuring the size of the obtained representation.

In this task, the time taken to serialize and deserialize the data is not considered – see the flat-serialization-throughput and flat-deserialization-throughput tasks for that aspect.

Metrics

  • Serialized representation size – the primary metric, size of the serialized RDF data, in bytes.
  • Compression ratio – additionally, the compression ratio can be calculated as the ratio of the reference size to the compressed size. The reference size is the size of the same data serialized using a baseline method, e.g., the N-Triples serialization format.
    • In the RDF literature, the "compression ratio" is often defined as the inverse of the above definition and expressed as a percentage. For example, a compression ratio of (50%) means that the compressed data is half the size of the reference data.

Results

See the results for this task reported by the community: RESULTS.

This task in benchmarks outside of RiverBench

  • In a paper about an RDF compression method using MapReduce, a compression benchmark was performed in Section 5.1. The authors have measured the output size of their method (in gigabytes) in comparison to the input data size.
    • Urbani, J., Maassen, J., Drost, N., Seinstra, F., & Bal, H. (2013). Scalable RDF data compression with MapReduce. Concurrency and Computation: Practice and Experience, 25(1), 24-39. https://doi.org/10.1002/cpe.2840
  • In the paper about the HDT binary format, such a benchmark was performed in Section 5. The "Compression Ratio" metric there refers to the ratio between the compressed data size and the reference data size, with N-Triples used as the reference.
    • Fernández, J. D., Martínez-Prieto, M. A., Gutiérrez, C., Polleres, A., & Arias, M. (2013). Binary RDF representation for publication and exchange (HDT). Journal of Web Semantics, 19, 22-41. https://doi.org/10.1016/j.websem.2013.01.002
  • In the paper about the ERI format, such a benchmark can be found in Section 4.2. The "Compression Ratio" metric there refers to the ratio between the compressed data size and the reference data size, with N-Triples used as the reference.
    • Fernández, J. D., Llaves, A., & Corcho, O. (2014). Efficient RDF interchange (ERI) format for RDF data streams. In The Semantic Web–ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II 13 (pp. 244-259). Springer International Publishing. https://doi.org/10.1007/978-3-319-11915-1_16

See also

Metadata

Info

Download this metadata in RDF: Turtle, N-Triples, RDF/XML, Jelly
Source repository: category-flat
Permanent URL: https://w3id.org/riverbench/v/dev/tasks/flat-compression

General information