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Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources

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The Semantic Web – ISWC 2019 (ISWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11779))

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Abstract

The last two decades witnessed a remarkable evolution in terms of data formats, modalities, and storage capabilities. Instead of having to adapt one’s application needs to the, earlier limited, available storage options, today there is a wide array of options to choose from to best meet an application’s needs. This has resulted in vast amounts of data available in a variety of forms and formats which, if interlinked and jointly queried, can generate valuable knowledge and insights. In this article, we describe Squerall: a framework that builds on the principles of Ontology-Based Data Access (OBDA) to enable the querying of disparate heterogeneous sources using a unique query language, SPARQL. In Squerall, original data is queried on-the-fly without prior data materialization or transformation. In particular, Squerall allows the aggregation and joining of large data in a distributed manner. Squerall supports out-of-the-box five data sources and moreover, it can be programmatically extended to cover more sources and incorporate new query engines. The framework provides user interfaces for the creation of necessary inputs, as well as guiding non-SPARQL experts to write SPARQL queries. Squerall is integrated into the popular SANSA stack and available as open-source software via GitHub and as a Docker image.

Software Framework. https://eis-bonn.github.io/Squerall.

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Notes

  1. 1.

    We used queue data structure simply to be able to dynamically pull (unqueue) elements from it iteratively till it has no more elements.

  2. 2.

    Available at https://github.com/EIS-Bonn/Squerall (Apache-2.0 license).

  3. 3.

    http://prestodb.io.

  4. 4.

    URL: http://purl.org/db/nosql, details are out of the scope of this article.

  5. 5.

    The 1.5M scale factor generates 500M RDF triples, and the 5M factor 1,75B triples.

  6. 6.

    See https://github.com/EIS-Bonn/Squerall/tree/master/evaluation.

  7. 7.

    https://github.com/SANSA-Stack/SANSA-DataLake.

  8. 8.

    www.big-data-europe.eu & https://github.com/big-data-europe.

  9. 9.

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

  10. 10.

    https://zenodo.org/record/2636436.

  11. 11.

    https://github.com/EIS-Bonn/Squerall/tree/master/evaluation/screencasts.

  12. 12.

    https://github.com/EIS-Bonn/Squerall/wiki/Extending-Squerall.

  13. 13.

    https://github.com/SDM-TIB/Ontario.

  14. 14.

    https://github.com/EIS-Bonn/Squerall/tree/master/evaluation.

  15. 15.

    https://spark-packages.org https://prestodb.io/docs/current/connector.

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Acknowledgment

This work is partly supported by the EU H2020 projects BETTER (GA 776280) and QualiChain (GA 822404); and by the ADAPT Centre for Digital Content Technology funded under the SFI Research Centres Programme (Grant 13/RC/2106) and co-funded under the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Smart Data Analytics (SDA) Group, Bonn University, Bonn, Germany

    Mohamed Nadjib Mami, Simon Scerri, Hajira Jabeen & Jens Lehmann

  2. Enterprise Information Systems, Fraunhofer IAIS, Sankt Augustin, Germany

    Mohamed Nadjib Mami, Damien Graux, Simon Scerri & Jens Lehmann

  3. ADAPT Centre, Trinity College of Dublin, Dublin, Ireland

    Damien Graux

  4. TIB, Hannover University, Hannover, Germany

    Sören Auer

Authors
  1. Mohamed Nadjib Mami
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  2. Damien Graux
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  4. Hajira Jabeen
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  5. Sören Auer
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  6. Jens Lehmann
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Corresponding author

Correspondence to Mohamed Nadjib Mami .

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Editors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  2. Linköping University, Linköping, Sweden

    Olaf Hartig

  3. University of Bonn, Bonn, Germany

    Maria Maleshkova

  4. University of Economics Prague, Prague, Czech Republic

    Vojtěch Svátek

  5. University of Illinois at Chicago, Chicago, IL, USA

    Isabel Cruz

  6. University of Chile, Santiago, Chile

    Aidan Hogan

  7. Memect Technology, Beijing, China

    Jie Song

  8. Mines Saint-Etienne, Saint-Etienne, France

    Maxime Lefrançois

  9. Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France

    Fabien Gandon

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Mami, M.N., Graux, D., Scerri, S., Jabeen, H., Auer, S., Lehmann, J. (2019). Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11779. Springer, Cham. https://doi.org/10.1007/978-3-030-30796-7_15

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  • DOI: https://doi.org/10.1007/978-3-030-30796-7_15

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