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Towards Semantification of Big Data Technology

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Book cover Big Data Analytics and Knowledge Discovery (DaWaK 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9829))

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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.

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Notes

  1. 1.

    http://newvantage.com/wp-content/uploads/2014/12/Big-Data-Survey-2014-Summary-Report-110314.pdf, https://www.capgemini-consulting.com/resource-file-access/resource/pdf/cracking_the_data_conundrum-big_data_pov_13-1-15_v2.pdf.

  2. 2.

    https://developers.google.com/transit/gtfs/.

  3. 3.

    http://www.w3.org/TR/r2rml/.

  4. 4.

    http://www.w3.org/2013/csvw/wiki/Main_Page.

  5. 5.

    http://www.w3.org/TR/json-ld/.

  6. 6.

    We disregard blank nodes, which can be avoided or replaced by IRIs [4].

  7. 7.

    A set of properties \(P_C\) of an RDF class C where: \(\forall \) p \(\in \) \(P_C\) (p rdfs:domain C).

  8. 8.

    mb and foaf are prefixes for mobility and friend of friend vocabularies, respectively.

  9. 9.

    When the object is occasionally not typed or is a URL.

  10. 10.

    https://spark.apache.org/.

  11. 11.

    https://www.mongodb.org.

  12. 12.

    http://lov.okfn.org/dataset/lov/terms.

  13. 13.

    http://developers.google.com/transit/gtfs/reference.

  14. 14.

    http://www.hpl.hp.com/techreports/2006/HPL-2006-140.html.

  15. 15.

    https://parquet.apache.org.

  16. 16.

    https://github.com/EIS-Bonn/SeBiDA.

  17. 17.

    Using a command line: ./generate -fc -pc [scaling factor] -s [file format] -fn [file name], where file format is nt for RDF data and xml for XML data. More details in: http://wifo5-03.informatik.uni-mannheim.de/bizer/berlinsparqlbenchmark/spec/BenchmarkRules/#datagenerator+.

  18. 18.

    https://github.com/gh-rdf3x/gh-rdf3x.

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Authors and Affiliations

  1. University of Bonn, Bonn, Germany

    Mohamed Nadjib Mami, Simon Scerri, Sören Auer & Maria-Esther Vidal

  2. Fraunhofer IAIS, Sankt Augustin, Germany

    Mohamed Nadjib Mami, Simon Scerri, Sören Auer & Maria-Esther Vidal

Authors
  1. Mohamed Nadjib Mami
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  2. Simon Scerri
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  3. Sören Auer
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  4. Maria-Esther Vidal
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Correspondence to Mohamed Nadjib Mami .

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Editors and Affiliations

  1. University of Science and Technology , Rolla, Missouri, USA

    Sanjay Madria

  2. Osaka University , Osaka, Japan

    Takahiro Hara

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Mami, M.N., Scerri, S., Auer, S., Vidal, ME. (2016). Towards Semantification of Big Data Technology. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2016. Lecture Notes in Computer Science(), vol 9829. Springer, Cham. https://doi.org/10.1007/978-3-319-43946-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-43946-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43945-7

  • Online ISBN: 978-3-319-43946-4

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