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Towards Semantification of Big Data Technology

  • Mohamed Nadjib Mami
  • Simon Scerri
  • Sören Auer
  • Maria-Esther Vidal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9829)

Abstract

Much attention has been devoted to support the volume and velocity dimensions of Big Data. As a result, a plethora of technology components supporting various data structures (e.g., key-value, graph, relational), modalities (e.g., stream, log, real-time) and computing paradigms (e.g., in-memory, cluster/cloud) are meanwhile available. However, systematic support for managing the variety of data, the third dimension in the classical Big Data definition, is still missing. In this article, we present SeBiDA, an approach for managing hybrid Big Data. SeBiDA supports the Semantification of Big Data using the RDF data model, i.e., non-semantic Big Data is semantically enriched by using RDF vocabularies. We empirically evaluate the performance of SeBiDA for two dimensions of Big Data, i.e., volume and variety; the Berlin Benchmark is used in the study. The results suggest that even in large datasets, query processing time is not affected by data variety.

Keywords

Query Processing Resource Description Framework Semantic Data Hadoop Distribute File System Resource Description Framework Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohamed Nadjib Mami
    • 1
    • 2
  • Simon Scerri
    • 1
    • 2
  • Sören Auer
    • 1
    • 2
  • Maria-Esther Vidal
    • 1
    • 2
  1. 1.University of BonnBonnGermany
  2. 2.Fraunhofer IAISSankt AugustinGermany

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