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

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.