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Stream Reasoning Playground

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The Semantic Web (ESWC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13261))

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Abstract

Stream Reasoning is a well established field not only in the Semantic Web, but is also adapted in the knowledge representation and reasoning and AI community in general. In the Semantic Web area, there have been valuable efforts in building data generators and benchmarks, however they are not well suited for evaluating more expressive stream reasoning approaches, since the focus is on a graph-based data model and more limited reasoning features, such as query answering. This paper aims at filling the gap, so the different communities can compare, discuss, and benchmark the various approaches for stream reasoning based on a common playground. We will present the stream reasoning playground that targets streaming reasoning as the first-class modelling and processing feature. Our playground includes an easy-to-extend platform for data stream generation with pluggable data formatters, whereby different data stream sources, and modelling problems for two interesting application scenarios, i.e., intelligent traffic management and vehicle stream data analytics, are provided. Furthermore, we present a more generic scenario for time-series data, where a workflow for streaming time-series data from various datasets is facilitated by using mapping functions. To illustrate a first application of the playground, we report on the usage experience of well-known stream reasoner developers in the “model and solve” Hackathon event of the annual Stream Reasoning workshop.

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Notes

  1. 1.

    https://www.python.org/dev/peps/pep-0255/.

  2. 2.

    https://www.docker.com/.

  3. 3.

    https://www.eclipse.org/sumo/.

  4. 4.

    A full list of attributes is given in https://github.com/patrik999/stream-reasoning-challenge/blob/master/hackathon-2021/Hackaton_Overview.pdf.

  5. 5.

    Additionally to the standard namespaces rdf, rdfs, and xsd, we have , , .

  6. 6.

    https://vision.semkg.org/.

  7. 7.

    https://github.com/RMLio/RMLStreamer.

  8. 8.

    https://github.com/commaai/comma2k19.

  9. 9.

    https://github.com/patrik999/stream-reasoning-challenge/blob/master/example-custom-scenario/workflow.ipynb.

  10. 10.

    http://streamreasoning.org/stream-reasoning-hackathon-2021.

  11. 11.

    http://streamreasoning.org/events/srw2021.

  12. 12.

    The questions and results are provided in https://github.com/patrik999/stream-reasoning-challenge/blob/master/hackathon-2021/Survey.pdf.

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Acknowledgements

This work was funded by the German Research Foundation under grant nr. 453130567 (COSMO), the German Ministry for Education and Research BIFOLD grant nr. 01IS18025A and 01IS18037A, the German Academic Exchange Service grant nr. 57440921, and the EU Horizon 2020 Research and Innovation program under grant nr. 779852 (IoTCrawler).

Author information

Authors and Affiliations

  1. Vienna University of Technology, Vienna, Austria

    Patrik Schneider

  2. Siemens AG Österreich, Vienna, Austria

    Patrik Schneider

  3. University of Oldenburg, Oldenburg, Germany

    Daniel Alvarez-Coello

  4. BMW Technologies E/E Architecture, Wire Harness, Garching, Germany

    Daniel Alvarez-Coello

  5. Technical University of Berlin, Berlin, Germany

    Anh Le-Tuan, Manh Nguyen-Duc & Danh Le-Phuoc

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  1. Patrik Schneider
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  2. Daniel Alvarez-Coello
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  3. Anh Le-Tuan
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  4. Manh Nguyen-Duc
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  5. Danh Le-Phuoc
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Corresponding author

Correspondence to Patrik Schneider .

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Editors and Affiliations

  1. University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Universidad Simón Bolívar, Leibniz Information Centre for Science and Technology, Hannover, Niedersachsen, Germany

    Maria-Esther Vidal

  3. Institut Polytechnique de Paris "DIG", Télécom ParisTech, Palaiseau, France

    Fabian Suchanek

  4. University of Southern California, Marina del Rey, CA, USA

    Pedro Szekley

  5. IBM Research - Thomas J. Watson Research, Yorktown Heights, NY, USA

    Pavan Kapanipathi

  6. LaSIGE, Fac de Ciencias,Edif C6, Pis0 3, Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  7. University of Nantes, Nantes, France

    Hala Skaf-Molli

  8. Aalto University, Espoo, Finland

    Minna Tamper

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Schneider, P., Alvarez-Coello, D., Le-Tuan, A., Nguyen-Duc, M., Le-Phuoc, D. (2022). Stream Reasoning Playground. In: Groth, P., et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_24

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  • DOI: https://doi.org/10.1007/978-3-031-06981-9_24

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