Sampling Semantic Data Stream: Resolving Overload and Limited Storage Issues

  • Naman Jain
  • Manuel Pozo
  • Raja Chiky
  • Zakia Kazi-Aoul
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

The Semantic Web technologies are being increasingly used for exploiting relations between data. In addition, new tendencies of real-time systems, such as social networks, sensors, cameras or weather information, are continuously generating data. This implies that data and links between them are becoming extremely vast. Such huge quantity of data needs to be analyzed, processed, as well as stored if necessary. In this paper, we propose sampling operators that allow us to drop RDF Triples from the incoming data. Thereby, helping us to reduce the load on existing engines like CQELS, C-SPARQL, which are able to deal with big and linked data. Hence, the processing efforts, time as well as required storage space will be reduced remarkably. We have proposed Uniform Random Sampling, Reservoir Sampling and Chain Sampling operators which may be implemented depending on the application.

Keywords

Big data Linked data-stream Processing time Sampling 

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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Naman Jain
    • 1
  • Manuel Pozo
    • 2
  • Raja Chiky
    • 2
  • Zakia Kazi-Aoul
    • 2
  1. 1.VIT UniversityVelloreIndia
  2. 2.ISEP—LISITEParisFrance

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