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Transactional and Incremental Type Inference from Data Updates

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Data Science (BICOD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9147))

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Abstract

A distinctive property of relational database systems is the ability to perform data updates and queries in atomic blocks called transactions, with the well known ACID properties. To date, the ability of systems performing reasoning to maintain the ACID properties even over data held within a relational database, has been largely ignored. This paper studies an approach to reasoning over data from OWL 2 ontologies held in a relational database, where the ACID properties of transactions are maintained. Taking an incremental approach to maintaining materialised views of the result of reasoning, the approach is demonstrated to support a query and reasoning performance comparable to or better than other OWL reasoning systems, yet adding the important benefit of supporting transactions.

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Notes

  1. 1.

    All experiments were processed on a machine with Intel i7-2600 CPU @ 3.40 GHz, 8 Cores, and 16 GB of memory, running Microsoft SQL Server 2014. SQOWL2 uses OWL API v3.4.3 for ontology loading and Pellet v2.3.1 for classification. For comparisons, we used OWLim-Lite v5.4.6486 and Stardog-Community v2.2.1.

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Authors and Affiliations

  1. Department of Computing, Imperial College London, London, UK

    Yu Liu & Peter McBrien

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  1. Yu Liu
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  2. Peter McBrien
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Correspondence to Yu Liu .

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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, United Kingdom

    Sebastian Maneth

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Liu, Y., McBrien, P. (2015). Transactional and Incremental Type Inference from Data Updates. In: Maneth, S. (eds) Data Science. BICOD 2015. Lecture Notes in Computer Science(), vol 9147. Springer, Cham. https://doi.org/10.1007/978-3-319-20424-6_20

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  • DOI: https://doi.org/10.1007/978-3-319-20424-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20423-9

  • Online ISBN: 978-3-319-20424-6

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