In Stream Reasoning (SR), empirical research on RDF Stream Processing (RSP) is attracting a growing attention. The SR community proposed methodologies and benchmarks to investigate the RSP solution space and improve existing approaches. In this paper, we present RSPLab, an infrastructure that reduces the effort required to design and execute reproducible experiments as well as share their results. RSPLab integrates two existing RSP benchmarks (LSBench and CityBench) and two RSP engines (C-SPARQL engine and CQELS). It provides a programmatic environment to: deploy in the cloud RDF Streams and RSP engines, interact with them using TripleWave and RSP Services, and continuously monitor their performances and collect statistics. RSPLab is released as open-source under an Apache 2.0 license.

RSPLab: RDF stream processing benchmarking made easy

Tommasini, Riccardo;Della Valle, Emanuele;Mauri, Andrea;Brambilla, Marco
2017

Abstract

In Stream Reasoning (SR), empirical research on RDF Stream Processing (RSP) is attracting a growing attention. The SR community proposed methodologies and benchmarks to investigate the RSP solution space and improve existing approaches. In this paper, we present RSPLab, an infrastructure that reduces the effort required to design and execute reproducible experiments as well as share their results. RSPLab integrates two existing RSP benchmarks (LSBench and CityBench) and two RSP engines (C-SPARQL engine and CQELS). It provides a programmatic environment to: deploy in the cloud RDF Streams and RSP engines, interact with them using TripleWave and RSP Services, and continuously monitor their performances and collect statistics. RSPLab is released as open-source under an Apache 2.0 license.
16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings
9783319682037
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1059299
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