Linked Data Management

  • Manfred Hauswirth
  • Marcin WylotEmail author
  • Martin Grund
  • Paul Groth
  • Philippe Cudré-Mauroux


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.


Resource Description Framework Link Data Query Execution Triple Pattern Provenance Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Manfred Hauswirth
    • 1
  • Marcin Wylot
    • 1
    Email author
  • Martin Grund
    • 1
  • Paul Groth
    • 1
  • Philippe Cudré-Mauroux
    • 1
  1. 1.Technical University of Berlin (TU Berlin)BerlinGermany

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