A Compressed, Inference-Enabled Encoding Scheme for RDF Stream Processing

  • Jérémy LhezEmail author
  • Xiangnan Ren
  • Badre Belabbess
  • Olivier Curé
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10250)


The number of sensors producing data streams at a high velocity keeps increasing. This paper describes an attempt to design an inference-enabled, distributed, fault-tolerant framework targeting RDF streams in the context of an industrial project. Our solution gives a special attention to the latency issue, an important feature in the context of providing reasoning services. Low latency is attained by compressing the scheme and data of processed streams with a dedicated semantic-aware encoding solution. After providing an overview of our architecture, we detail our encoding approach which supports a trade-off between two common inference methods, i.e., materialization and query reformulation. The analysis of results of our prototype emphasize the relevance of our design choices.



This work has been supported by the Waves project which is partially supported by the French FUI (Fonds Unique Interministériel) call #17.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jérémy Lhez
    • 1
    Email author
  • Xiangnan Ren
    • 1
    • 2
    • 3
  • Badre Belabbess
    • 1
    • 2
  • Olivier Curé
    • 1
  1. 1.LIGM (UMR 8049), CNRS, ENPC, ESIEE, UPEMMarne-la-valléeFrance
  2. 2.AtosBezonsFrance
  3. 3.ISEP - LISITEParisFrance

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