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Towards Answering Provenance-Enabled SPARQL Queries Over RDF Data Cubes

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Semantic Technology (JIST 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10055))

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Abstract

The SPARQL 1.1 standard has made it possible to formulate analytical queries in SPARQL. While some approaches have become available for processing analytical queries on RDF data cubes, little attention has been paid to answering provenance-enabled queries over such data. Yet, considering provenance is a prerequisite to being able to validate if a query result is trustworthy. The main challenge for existing triple stores is the way provenance can be encoded in standard triple stores based on context values (named graphs). Hence, in this paper we analyze the suitability of existing triple stores for answering provenance-enabled queries on RDF data cubes, identify their shortcomings, and propose an index to handle the high number of context values that provenance encoding typically entails. Our experimental results using the Star Schema Benchmark show the feasibility and scalability of our index and query evaluation strategies.

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Notes

  1. 1.

    https://github.com/lorenae/qb4olap/blob/master/rdf/qb4olap.1.2.ttl.

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Acknowledgments

This research was partially funded by the Danish Council for Independent Research (DFF) under grant agreement No. DFF-4093-00301.

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Authors and Affiliations

  1. Department of Computer Science, Aalborg University, Aalborg, Denmark

    Kim Ahlstrøm, Katja Hose & Torben Bach Pedersen

Authors
  1. Kim Ahlstrøm
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  2. Katja Hose
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  3. Torben Bach Pedersen
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Corresponding author

Correspondence to Kim Ahlstrøm .

Editor information

Editors and Affiliations

  1. Information Technology, Monash University, Melbourne, Victoria, Australia

    Yuan-Fang Li

  2. Computer Science and Technology, Nanjing University, Nanjing, China

    Wei Hu

  3. Computer Science, National University of Singapore, Singapore, Singapore

    Jin Song Dong

  4. University of Huddersfield, Huddersfield, United Kingdom

    Grigoris Antoniou

  5. Information and Communication Technology, Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  6. ISTD, Singapore University of Technology and Design, Singapore, Singapore

    Jun Sun

  7. Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yang Liu

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Ahlstrøm, K., Hose, K., Pedersen, T.B. (2016). Towards Answering Provenance-Enabled SPARQL Queries Over RDF Data Cubes. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-50112-3_14

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