RDF Stream Processing (RSP) is gaining momentum. The RDF stream data model is progressively adopted and many SPARQL extensions for continuous querying are converging into a unified RSP query language. However, the RSP community still has to investigate when transforming data streams in RDF streams pays off. In this paper, we report on several experiments on a revolutionized version of our Streaming Linked Data framework (namely, SLD Revolution). SLD Revolution adopts i) Generic Programming, i.e. it operates on time-stamped generic data items, and ii) it applies a lazy-transformation approach, i.e. it postpones the RDF stream transformation until it can benefit from it., processing data according to their nature (event-, tuple-, tree- and graph-based). SLD Revolution results to be a cheaper (it uses less memory and has a smaller CPU load), faster (it reaches higher maximum input throughput) yet more accurate (it provides a smaller error rate in the results) solution than its ancestor SLD.

SLD revolution: A cheaper, faster yet more accurate streaming linked data framework

Balduini, Marco;Della Valle, Emanuele;Tommasini, Riccardo
2017

Abstract

RDF Stream Processing (RSP) is gaining momentum. The RDF stream data model is progressively adopted and many SPARQL extensions for continuous querying are converging into a unified RSP query language. However, the RSP community still has to investigate when transforming data streams in RDF streams pays off. In this paper, we report on several experiments on a revolutionized version of our Streaming Linked Data framework (namely, SLD Revolution). SLD Revolution adopts i) Generic Programming, i.e. it operates on time-stamped generic data items, and ii) it applies a lazy-transformation approach, i.e. it postpones the RDF stream transformation until it can benefit from it., processing data according to their nature (event-, tuple-, tree- and graph-based). SLD Revolution results to be a cheaper (it uses less memory and has a smaller CPU load), faster (it reaches higher maximum input throughput) yet more accurate (it provides a smaller error rate in the results) solution than its ancestor SLD.
CEUR Workshop Proceedings
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1062476
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