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FedTopK: Top-K Queries Optimization over Federated RDF Systems

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Database Systems for Advanced Applications (DASFAA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12683))

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Abstract

Recently, how to evaluate SPARQL queries over federated RDF systems has become a hot research topic. However, most existing studies mainly focus on implementing and optimizing the basic queries over federated SPARQL systems, and few of them discuss top-k queries. To remedy this defect, this demo designs a system named FedTopK that can support top-k queries over federated RDF systems. FedTopK employs a cost-based optimal query plan generation algorithm and a query plan execution optimization strategy to minimize the top-k query cost. In addition, FedTopK uses a query decomposition optimization scheme which allow merge triple patterns with the same multi-sources into one subquery to reduce the remote access times. Experimental studies over real federated RDF datasets show that the demo is efficient.

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References

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Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant (No. U20A20174, 61772191), Science and Technology Key Projects of Hunan Province (2019WK2072, 2018TP3001, 2018TP2023,), ChangSha Science and Technology Project (kq2006029), National Key Research and Development Program of China under grant 2019YFB1406401 and Key Research and Development Program of Hubei Province (No. 2020BAB026).

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Authors and Affiliations

  1. Hunan University, Changsha, China

    Ningchao Ge, Zheng Qin & Peng Peng

  2. Peking University, Beijing, China

    Lei Zou

Authors
  1. Ningchao Ge
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  2. Zheng Qin
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  3. Peng Peng
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  4. Lei Zou
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Corresponding author

Correspondence to Zheng Qin .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  4. The Pennsylvania State University, University Park, PA, USA

    Wang-Chien Lee

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  6. Athens University of Economics and Business, Athens, Greece

    Vana Kalogeraki

  7. National Cheng Kung University, Tainan City, Taiwan

    Jen-Wei Huang

  8. National Tsing Hua University, Hsinchu, Taiwan

    Chih-Ya Shen

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Ge, N., Qin, Z., Peng, P., Zou, L. (2021). FedTopK: Top-K Queries Optimization over Federated RDF Systems. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_42

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  • DOI: https://doi.org/10.1007/978-3-030-73200-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73199-1

  • Online ISBN: 978-3-030-73200-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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