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A cache-based method to improve query performance of linked Open Data cloud

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Abstract

The proliferation of semantic big data has resulted in a large amount of content published over the Linked Open Data (LOD) cloud. Semantic Web applications consume these data by issuing SPARQL queries. One of the main challenges faced by querying the LOD web cloud on account of the inherent distributed nature of LOD is its high search latency and lack of tools to connect the SPARQL endpoints. In this paper, we propose an Adaptive Cache Replacement strategy (ACR) that aims to accelerate the overall query processing of the LOD cloud. ACR alleviates the burden on SPARQL endpoints by identifying subsequent queries learned from clients historical query patterns and caching the result of these queries. For cache replacement, we propose an exponential smoothing forecasting method to replace the less valuable cache content. In the experimental study, we evaluate the performance of the proposed approach in terms of hit rates, query time and overhead. The proposed approach is found to outperform existing state-of-the-art approaches, increase hit rates by 5.46%, and reduce the query times by 6.34%.

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Notes

  1. https://linkedgeodata.org/.

  2. https://www.w3.org/RDF/.

  3. https://www.w3.org/TR/rdf-sparql-query/.

  4. https://wiki.dbpedia.org/.

  5. https://www.w3.org/TR/sparql11-overview/.

  6. http://www.w3.org/TR/rdf-sparql-query/.

  7. http://usewod.org/usewod2014.html.

  8. https://jena.apache.org/.

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Acknowledgements

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information & communications Technology Promotion). This work was supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00655), NRF-2016K1A3A7A03951968 & NRF-2019R1A2C2090504.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do, 446-701, Republic of Korea

    Usman Akhtar, Muhammad Asif Razzaq, Jaehun Bang & Sungyoung Lee

  2. Viniam Consulting AB, Halmstad, Sweden

    Anita Sant’Anna

  3. Department of Industrial and Management System Engineering, Kyung Hee University, Yongin-si, South Korea

    Chang-Ho Jihn

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  1. Usman Akhtar
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  2. Anita Sant’Anna
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  3. Chang-Ho Jihn
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  4. Muhammad Asif Razzaq
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  5. Jaehun Bang
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  6. Sungyoung Lee
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Correspondence to Jaehun Bang or Sungyoung Lee.

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Akhtar, U., Sant’Anna, A., Jihn, CH. et al. A cache-based method to improve query performance of linked Open Data cloud. Computing 102, 1743–1763 (2020). https://doi.org/10.1007/s00607-020-00814-9

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