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Towards Knowledge-Driven Automatic Service Composition for Wildfire Prediction

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Service-Oriented Computing – ICSOC 2020 Workshops (ICSOC 2020)

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Abstract

Wildfire prediction from Earth Observation (EO) data has gained much attention in the past years, through the development of connected sensors and weather satellites. Nowadays, it is possible to extract knowledge from collected EO data and to learn from this knowledge without human intervention to trigger wildfire alerts. However, exploiting knowledge extracted from multiple EO data sources at run-time and predicting wildfire raise multiple challenges. One major challenge is to provide dynamic construction of service composition plans, according to the data obtained from sensors. In this paper, we present a knowledge-driven Machine Learning approach that relies on historical data related to wildfire observations to guide the collection of EO data and to automatically and dynamically compose services for triggering wildfire alerts.

This work was financially supported by the “PHC Utique” program of the French Ministry of Foreign Affairs and Ministry of higher education and research and the Tunisian Ministry of higher education and scientific research in the CMCU project number 17G1122. The authors acknowledge the European Commission for funding the InnoRenew CoE project (Grant Agreement #739574) under the Horizon2020 Widespread-Teaming program and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European regional Development Fund).

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Notes

  1. 1.

    https://sites.google.com/view/predicat/predicat.

  2. 2.

    Service composition is the combination of a set of the smallest services forming a more complex service to meet users’ complex requirements.

  3. 3.

    Qualities of the data sources and services are out of the scope of this paper.

  4. 4.

    https://docs.google.com/spreadsheets/d/1v-46-KMHtErt3IGigFsusk7Fnp61DKvctMs9KMH_a-E/edit?usp=sharing.

  5. 5.

    https://research.csiro.au/amicus/.

  6. 6.

    Computing the most important feature and executing its service.

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Author information

Authors and Affiliations

  1. MIRACL Laboratory, FSEG Sfax, Sfax University, Sfax, Tunisia

    Hela Taktak, Khouloud Boukadi & Faïez Gargouri

  2. Lyon University, Lyon 3 University, LIRIS UMR5205, Lyon, France

    Hela Taktak & Chirine Ghedira Guégan

  3. InnoRenew CoE, University of Primorska, Livade 6, 6310 Izola, 6000, Koper, Slovenia

    Michael Mrissa

Authors
  1. Hela Taktak
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  2. Khouloud Boukadi
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  3. Chirine Ghedira Guégan
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  4. Michael Mrissa
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  5. Faïez Gargouri
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Corresponding author

Correspondence to Hela Taktak .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Zayed University, Dubai, United Arab Emirates

    Fatma Outay

  3. University of New South Wales, Sydney, NSW, Australia

    Hye-young Paik

  4. Huawei Paris Research Center, Paris, France

    Amira Alloum

  5. National Research Council C.N.R., Pisa, Italy

    Marinella Petrocchi

  6. Deakin University, Waurn Ponds, VIC, Australia

    Mohamed Reda Bouadjenek

  7. Macquarie University, Sydney, NSW, Australia

    Amin Beheshti

  8. Rochester Institute of Technology, Rochester, NY, USA

    Xumin Liu

  9. Université de Paris, Paris, France

    Abderrahmane Maaradji

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Taktak, H., Boukadi, K., Guégan, C.G., Mrissa, M., Gargouri, F. (2021). Towards Knowledge-Driven Automatic Service Composition for Wildfire Prediction. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2020 Workshops. ICSOC 2020. Lecture Notes in Computer Science(), vol 12632. Springer, Cham. https://doi.org/10.1007/978-3-030-76352-7_38

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  • DOI: https://doi.org/10.1007/978-3-030-76352-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76351-0

  • Online ISBN: 978-3-030-76352-7

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