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Adaptive Workload-Based Partitioning and Replication for RDF Graphs

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Database and Expert Systems Applications (DEXA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11030))

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Abstract

Distributed processing of RDF data requires partitioning of big and complex data sets. The partitioning affects the performance of the distributed query processing and the amount of data transfer between the network-connected nodes. Static graph partitioning aims to generate partitions with lowest number of edges in between but suffers high communication cost when a query trespasses a partition’s border, because then it requires moving partial results across the network. Workload-aware partitioning is an alternative but faces complex decisions regarding the storage space and the workload orientation. In this paper, we present an adaptive partitioning and replication strategy on three levels. We initialize our system with static partitioning where it collects and analyzes the received workload; then we let it adapt itself with two levels of dynamic replications, besides applying a weighting system to its initial static partitioning to decrease the ratio of border nodes.

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Notes

  1. 1.

    https://code.google.com/archive/p/rdf3x/.

  2. 2.

    https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/.

References

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Acknowledgements

The authors would like to thank Deutscher Akademischer Austauschdienst (DAAD) for providing fund for research on this project.

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Authors and Affiliations

  1. Institute of Computer Science, University of Göttingen, Göttingen, Germany

    Ahmed Al-Ghezi & Lena Wiese

Authors
  1. Ahmed Al-Ghezi
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  2. Lena Wiese
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Corresponding author

Correspondence to Ahmed Al-Ghezi .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

  3. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Regensburg, Regensburg, Germany

    Günther Pernul

  5. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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Al-Ghezi, A., Wiese, L. (2018). Adaptive Workload-Based Partitioning and Replication for RDF Graphs. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11030. Springer, Cham. https://doi.org/10.1007/978-3-319-98812-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-98812-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98811-5

  • Online ISBN: 978-3-319-98812-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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