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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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/).
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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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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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