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Handbook of Big Data Technologies pp 307–338Cite as

Linked Data Management

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Abstract

The size of Linked Data is growing exponentially, thus a Linked Data management system has to be able to deal with increasing amounts of data. Additionally, in the Linked Data context, variety is especially important. In spite of its seemingly simple data model, Linked Data actually encodes rich and complex graphs mixing both instance and schema-level data. Since Linked Data is schema-free (i.e., the schema is not strict), standard databases techniques cannot be directly adopted to manage it. Even though organizing Linked Data in a form of a table is possible, querying a giant triple table becomes very costly due to the multiple nested joins required typical queries. The heterogeneity of Linked Data poses also entirely new challenges to database systems, where managing provenance information is becoming a requirement. Linked Data queries usually include multiple sources and results can be produced in various ways for a specific scenario. Such heterogeneous data can incorporate knowledge on provenance, which can be further leveraged to provide users with a reliable and understandable description of the way the query result was derived, and improve the query execution performance due to high selectivity of provenance information. In this chapter, we provide a detailed overview of current approaches specifically designed for Linked Data management. We focus on storage models, indexing techniques, and query execution strategies. Finally, we provide an overview of provenance models, definitions, and serialization techniques for Linked Data. We also survey the database management systems implementing techniques to manage provenance information in the context of Linked Data.

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Notes

  1. 1.

    http://www.w3.org/Addressing/.

  2. 2.

    http://www.w3.org/Protocols/.

  3. 3.

    http://www.w3.org/RDF/.

  4. 4.

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

  5. 5.

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

  6. 6.

    In databases indexes are used to locate data without scanning the entire dataspace, thus to improve the speed of retrieval operations. For more details about database concepts we refer the reader to the positions in the literature introduced in Sect. 2.

  7. 7.

    Optimization statistics are used in databases to choose the best execution plan for a query. They contain information describing distribution of various objects in a database.

  8. 8.

    From the database perspective a context can be seen as a graph, thus there is no difference between those two concepts in terms of data management. Here, we keep the original terminology of the authors of each approach.

  9. 9.

    http://bmagic.sourceforge.net/dGap.html.

  10. 10.

    https://jena.apache.org/documentation/tdb/.

  11. 11.

    http://dublincore.org/documents/dcmi-terms/.

  12. 12.

    http://inference-web.org/wiki/PML_3.0.

  13. 13.

    http://www.w3.org/TR/hcls-dataset/.

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  1. Technical University of Berlin (TU Berlin), Berlin, Germany

    Manfred Hauswirth, Marcin Wylot, Martin Grund, Paul Groth & Philippe Cudré-Mauroux

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    Albert Y. Zomaya

  2. The School of Computer Science, The University of New South Wales, Eveleigh, New South Wales, Australia

    Sherif Sakr

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Hauswirth, M., Wylot, M., Grund, M., Groth, P., Cudré-Mauroux, P. (2017). Linked Data Management. In: Zomaya, A., Sakr, S. (eds) Handbook of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-49340-4_9

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