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Big Data Integration for Industry 4.0

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Digital Transformation

Abstract

The fourth industrial revolution promises a new quality of automation with smart manufacturing devices sharing enormous amounts of data. A crucial step in fulfilling this promise is developing advanced data integration methods that are able to consolidate and combine heterogeneous data from multiple sources. We outline the use of knowledge graphs for data integration and provide an overview of proposed approaches to create and update such knowledge graphs, in particular for schema and ontology matching, data lifting and especially for entity resolution. Furthermore, we present data integration use cases for Industry 4.0 and discuss open problems.

This work was supported by the German Federal Ministry of Education and Research (BMBF, 01/S18026A-F) by funding the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig.

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Notes

  1. 1.

    https://ontop-vkg.org

  2. 2.

    https://www.w3.org/TR/rdf-primer/

  3. 3.

    https://www.w3.org/TR/rdf11-concepts/#vocabularies

  4. 4.

    http://www.w3.org/TR/rdb-direct-mapping/

  5. 5.

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

  6. 6.

    https://spark.apache.org/

  7. 7.

    https://flink.apache.org/

  8. 8.

    https://neo4j.com/

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Authors and Affiliations

  1. Institute for Computer Science, Leipzig University, Leipzig, Germany

    Alieh Saeedi, Victor Christen & Erhard Rahm

  2. ScaDS.AI, Leipzig University, Leipzig, Germany

    Daniel Obraczka

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  1. Daniel Obraczka
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  2. Alieh Saeedi
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  3. Victor Christen
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  4. Erhard Rahm
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Correspondence to Erhard Rahm .

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  1. Technische Universität München, Garching b. München, Bayern, Germany

    Birgit Vogel-Heuser

  2. Johannes Kepler University Linz, Linz, Oberösterreich, Austria

    Manuel Wimmer

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Obraczka, D., Saeedi, A., Christen, V., Rahm, E. (2023). Big Data Integration for Industry 4.0. In: Vogel-Heuser, B., Wimmer, M. (eds) Digital Transformation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-65004-2_10

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  • DOI: https://doi.org/10.1007/978-3-662-65004-2_10

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  • Publisher Name: Springer Vieweg, Berlin, Heidelberg

  • Print ISBN: 978-3-662-65003-5

  • Online ISBN: 978-3-662-65004-2

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