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SLD Revolution: A Cheaper, Faster yet More Accurate Streaming Linked Data Framework

  • Marco Balduini
  • Emanuele Della Valle
  • Riccardo Tommasini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10577)

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.

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marco Balduini
    • 1
  • Emanuele Della Valle
    • 1
  • Riccardo Tommasini
    • 1
  1. 1.DEIB, Politecnico of MilanoMilanoItaly

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