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Capturing and Querying Uncertainty in RDF Stream Processing

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Knowledge Engineering and Knowledge Management (EKAW 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12387))

Abstract

RDF Stream Processing (RSP) has been proposed as a candidate for bringing together the Complex Event Processing (CEP) paradigm and the Semantic Web standards. In this paper, we investigate the impact of explicitly representing and processing uncertainty in RSP for the use in CEP. Additionally, we provide a representation for capturing the relevant notions of uncertainty in the RSP-QL\(^\star \) data model and describe query functions that can operate on this representation. The impact evaluation is based on a use-case within electronic healthcare, where we compare the query execution overhead of different uncertainty options in a prototype implementation. The experiments show that the influence on query execution performance varies greatly, but that uncertainty can have noticeable impact on query execution performance. On the other hand, the overhead grows linearly with respect to the stream rate for all uncertainty options in the evaluation, and the observed performance is sufficient for many use-cases. Extending the representation and operations to support more uncertainty options and investigating different query optimization strategies to reduce the impact on execution performance remain important areas for future research.

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Notes

  1. 1.

    http://ecareathome.se/.

  2. 2.

    https://www.w3.org/community/rsp/.

  3. 3.

    https://jena.apache.org/documentation/query/writing_functions.html.

  4. 4.

    https://rdf4j.eclipse.org/documentation/custom-sparql-functions/.

  5. 5.

    http://vos.openlinksw.com/owiki/wiki/VOS/VirtTipsAndTricksGuideCustomSPARQLExtensionFunction.

  6. 6.

    https://www.w3id.org/rsp/rspu.

  7. 7.

    https://github.com/keski/RSPQLStarEngine.

  8. 8.

    https://commons.apache.org/proper/commons-math/ (version 3.6.1).

  9. 9.

    https://www.bayesfusion.com/.

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Authors and Affiliations

  1. Linköping University, Linköping, Sweden

    Robin Keskisärkkä, Eva Blomqvist, Leili Lind & Olaf Hartig

  2. RISE Research Institutes of Sweden/Division Digital Systems, Linköping, Sweden

    Leili Lind

Authors
  1. Robin Keskisärkkä
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  2. Eva Blomqvist
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  3. Leili Lind
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  4. Olaf Hartig
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Corresponding author

Correspondence to Robin Keskisärkkä .

Editor information

Editors and Affiliations

  1. University of Cape Town, Cape Town, South Africa

    C. Maria Keet

  2. Institute of Data Science, Maastricht University, Maastricht, The Netherlands

    Michel Dumontier

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Keskisärkkä, R., Blomqvist, E., Lind, L., Hartig, O. (2020). Capturing and Querying Uncertainty in RDF Stream Processing. In: Keet, C.M., Dumontier, M. (eds) Knowledge Engineering and Knowledge Management. EKAW 2020. Lecture Notes in Computer Science(), vol 12387. Springer, Cham. https://doi.org/10.1007/978-3-030-61244-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-61244-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61243-6

  • Online ISBN: 978-3-030-61244-3

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