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Exploiting IoT Data Crossings for Gradual Pattern Mining Through Parallel Processing

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ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium (TPDL 2020, ADBIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1260))

Abstract

Today, with the proliferation of Internet of Things (IoT) applications in almost every area of our society comes the trouble of deducing relevant information from real-time time-series data (from different sources) for decision making. In this paper, we propose a fuzzy temporal approach for crossing such data sets with the ultimate goal of exploiting them for temporal gradual pattern mining. A temporal gradual pattern may take the form: “the higher the humidity, the lower the temperature, almost 15 min later”. In addition, we apply parallel processing on our implementation and measure its computational performance.

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Availability of Materials

The source code for our FuzzTX algorithm is available at our GitHub repository: https://github.com/owuordickson/data-crossing.git. All the results of our test runs are available at our GitHub link: https://github.com/owuordickson/meso-hpc-lr/tree/master/results/fuzztx. Data employed in the research study came from OREME’s Coastline Observation System (https://oreme.org/observation/ltc/) and an OREME observatory which recorded the meteorological measurements at the Puéchabon site. This data is licensed under a Creative Commons Attribution 4.0 License and the site is annually supported by Ecofor, Allenvi and ANAEE-F (http://www.anaee-france.fr/fr/).

Notes

  1. 1.

    https://oreme.org.

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Acknowledgment

This work is part of a Ph.D. thesis and the authors would like to thank the French Government through the office of Co-operation and Cultural Service (Kenya) and the office of Campus France (Montpellier) for their involvement in creating the opportunity for this work to be produced. This work has been realized with the support of the High Performance Computing Platform: MESO@LR (https://meso-lr.umontpellier.fr/faq/), financed by the Occitanie/Pyrénées-Méditerranée Region, Montpellier Mediterranean Metropole and Montpellier University.

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Authors and Affiliations

  1. LIRMM Univ. Montpellier, CNRS, Montpellier, France

    Dickson Odhiambo Owuor & Anne Laurent

  2. Faculty of IT, Strathmore University, Nairobi, Kenya

    Dickson Odhiambo Owuor & Joseph Onderi Orero

Authors
  1. Dickson Odhiambo Owuor
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  2. Anne Laurent
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  3. Joseph Onderi Orero
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Corresponding author

Correspondence to Dickson Odhiambo Owuor .

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Editors and Affiliations

  1. ISAE-ENSMA, Poitiers, France

    Ladjel Bellatreche

  2. Slovak University of Technology, Bratislava, Slovakia

    Mária Bieliková

  3. Université Lumière Lyon 2, Lyon, France

    Omar Boussaïd

  4. University of Genova, Genova, Italy

    Barbara Catania

  5. Université Lumière Lyon 2, Lyon, France

    Jérôme Darmont

  6. Leibniz University of Hannover, Hannover, Niedersachsen, Germany

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  7. Université Claude Bernard Lyon 1, Lyon, France

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  8. The Open University, Milton Keynes, UK

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  9. University of Ljubljana, Ljubljana, Slovenia

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  10. National Research University Higher School of Economics, St. Petersburg, Russia

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  11. Ionian University, Corfu, Greece

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  12. Goethe University Frankfurt, Frankfurt am Main, Hessen, Germany

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  13. Universitat Politècnica de Catalunya, Barcelona, Spain

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  14. AgroParisTech, Montpellier, France

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  15. University of Lyon, Lyon, France

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  16. Poznań University of Technology, Poznań, Poland

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  17. University of Ljubljana, Ljubljana, Slovenia

    Maja Žumer

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Owuor, D.O., Laurent, A., Orero, J.O. (2020). Exploiting IoT Data Crossings for Gradual Pattern Mining Through Parallel Processing. In: Bellatreche, L., et al. ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium. TPDL ADBIS 2020 2020. Communications in Computer and Information Science, vol 1260. Springer, Cham. https://doi.org/10.1007/978-3-030-55814-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-55814-7_9

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