Abstract
The RDF Stream Processing (RSP) is gaining momentum. The RDF stream data model is progressively adopted and many SPARQL extensions for continuous querying are converging to 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 (i) operates on time-stamped generic data items (events, tuples, trees and graphs), and (ii) it applies a lazy-transformation approach, i.e. it processes data according to their nature as long as possible. 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.
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Acknowledgement
We thank the reviewers of the 2\(^{nd}\) RDF Stream Processing Workshop co-located with ESWC 2017 for their valuable comments. They allowed us to refine this version of [22] for the ESWC 2017 workshops post-proceedings.
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Balduini, M., Della Valle, E., Tommasini, R. (2017). SLD Revolution: A Cheaper, Faster yet More Accurate Streaming Linked Data Framework. In: Blomqvist, E., Hose, K., Paulheim, H., Ławrynowicz, A., Ciravegna, F., Hartig, O. (eds) The Semantic Web: ESWC 2017 Satellite Events. ESWC 2017. Lecture Notes in Computer Science(), vol 10577. Springer, Cham. https://doi.org/10.1007/978-3-319-70407-4_37
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