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Facilitating Data Exploration in Industry 4.0

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Advances in Conceptual Modeling (ER 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11787))

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Abstract

Industrial Internet of Things (IIoT) devices operating in manufacturing plants allow capturing raw data generated by machines, regarding some indicators of interest. Multi-purpose dashboards facilitate a real-time visualization of all those raw data captured, and thus provide knowledge of each indicator. However, sometimes, domain experts interested in analyzing data belonging to specific domains find those dashboards too rigid. In this paper, we present a proposal that we have developed in a real Industry 4.0 scenario. Due to the customized visualizations that it provides, it enables domain experts to gain a greater value and insights out of the captured data. The core of the system is a new ontology that we have built, where, among others, the sensors used to capture indicators about the performance of a machine have been modeled. This semantic description allows to provide customized representations of the manufacturing machine, query formulation at a higher level of abstraction and customized graphical visualizations of the results.

Supported by the Spanish Ministry of Economy and Competitiveness (MEC) under Grant No.: FEDER/TIN2016-78011-C4-2-R. The work of Víctor Julio Ramírez is funded by the contract with reference BES-2017-081193.

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Notes

  1. 1.

    https://logz.io/blog/grafana-vs-kibana/.

  2. 2.

    In this paper the term ontology refers to the knowledge base composed of the conceptual level (i.e., axioms for classes and properties) and the instance level (i.e., assertions about individuals).

  3. 3.

    http://bdi.si.ehu.es/bdi/ontologies/ExtruOnt/ExtruOnt.

  4. 4.

    A machine that performs the extrusion process, in which some material is forced through a series of dies in order to create a desired shape.

  5. 5.

    https://virtuoso.openlinksw.com/.

  6. 6.

    https://www.w3.org/TR/sparql11-query/.

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Authors and Affiliations

  1. University of the Basque Country UPV/EHU, 20018, Donostia - San Sebastián, Spain

    Idoia Berges, Víctor Julio Ramírez-Durán & Arantza Illarramendi

Authors
  1. Idoia Berges
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  2. Víctor Julio Ramírez-Durán
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  3. Arantza Illarramendi
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Corresponding author

Correspondence to Víctor Julio Ramírez-Durán .

Editor information

Editors and Affiliations

  1. Free University of Bozen-Bolzano, Bolzano, Italy

    Giancarlo Guizzardi

  2. Ghent University, Ghent, Belgium

    Frederik Gailly

  3. Federal University of Southern Bahia, Salvador, Brazil

    Rita Suzana Pitangueira Maciel

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Berges, I., Ramírez-Durán, V.J., Illarramendi, A. (2019). Facilitating Data Exploration in Industry 4.0. In: Guizzardi, G., Gailly, F., Suzana Pitangueira Maciel, R. (eds) Advances in Conceptual Modeling. ER 2019. Lecture Notes in Computer Science(), vol 11787. Springer, Cham. https://doi.org/10.1007/978-3-030-34146-6_11

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  • DOI: https://doi.org/10.1007/978-3-030-34146-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34145-9

  • Online ISBN: 978-3-030-34146-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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