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Energy-Efficiency of OWL Reasoners—Frequency Matters

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Semantic Technology (JIST 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10675))

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Abstract

While running times of ontology reasoners have been studied extensively, studies on energy-consumption of reasoning are scarce, and the energy-efficiency of ontology reasoning is not fully understood yet. Earlier empirical studies on the energy-consumption of ontology reasoners focused on reasoning on smart phones and used measurement methods prone to noise and side-effects. This paper presents an evaluation of the energy-efficiency of five state-of-the-art OWL reasoners on an ARM single-board computer that has built-in sensors to measure the energy consumption of CPUs and memory precisely. Using such a machine gives full control over installed and running software, active clusters and CPU frequencies, allowing for a more precise and detailed picture of the energy consumption of ontology reasoning. Besides evaluating the energy consumption of reasoning, our study further explores the relationship between computation power of the CPU, reasoning time, and energy consumption.

This work is supported (in part) by the German Research Foundation (DFG) within the Collaborative Research Center SFB 912 HAEC.

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Notes

  1. 1.

    See http://lat.inf.tu-dresden.de/~koopmann/energy-evaluation/.

  2. 2.

    http://jfact.sourceforge.net/.

  3. 3.

    Note that this can lead to a different ranking as the one obtained at ORE’15.

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Authors and Affiliations

  1. Institute of Theoretical Computer Science, Technische Universität Dresden, Dresden, Germany

    Patrick Koopmann & Anni-Yasmin Turhan

  2. Operating Systems Group, Technische Universität Dresden, Dresden, Germany

    Marcus Hähnel

Authors
  1. Patrick Koopmann
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  2. Marcus Hähnel
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  3. Anni-Yasmin Turhan
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Correspondence to Anni-Yasmin Turhan .

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Editors and Affiliations

  1. Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  2. Dresden University of Technology, Dresden, Germany

    Anni-Yasmin Turhan

  3. Griffith University, Brisbane, Queensland, Australia

    Kewen Wang

  4. Tianjin University, Tianjin, China

    Xiaowang Zhang

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Koopmann, P., Hähnel, M., Turhan, AY. (2017). Energy-Efficiency of OWL Reasoners—Frequency Matters. In: Wang, Z., Turhan, AY., Wang, K., Zhang, X. (eds) Semantic Technology. JIST 2017. Lecture Notes in Computer Science(), vol 10675. Springer, Cham. https://doi.org/10.1007/978-3-319-70682-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-70682-5_6

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  • Online ISBN: 978-3-319-70682-5

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