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A knowledge-driven service composition framework for wildfire prediction

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Abstract

Wildfire prediction has drawn a lot of researchers’ interest, but still presents a computational difficulty since it necessitates real-time data collected from several distributed data sources. Furthermore, because environmental Web services have, now, access to a wider range of environmental data sources, services might be functionally similar but of varying quality. In this paper, we propose a knowledge-driven framework for service composition that is based on a layered architecture. Based on these layers, the proposed framework aims to select the optimal service instances participating in a service composition schema, through a modular ontology to infer the quality of data sources (QoDS) and an outranking approach. Moreover, it aims to executing the service composition schema at runtime by dynamically readjusting both the service composition schema and the service instances via a machine learning-based service composition approach. The conducted experiments showed that the proposed framework enables (i) a reasonable reasoning time for assessing the data sources’ quality, (ii) a decrease in the ELECTRE III MCDM method’s execution time achieved by combining the skyline and \(\alpha \)-dominance methods, (iii) dynamic generation of the most relevant service composition schema with the appropriate wildfire risk classes, and (iv) a high prediction accuracy using our proposed outranking approach compared to the randomly selected services.

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Notes

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

  2. https://data.chc.ucsb.edu/products/CHIRPS-2.0

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Acknowledgments

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).

Funding

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.

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Authors and Affiliations

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

    Hela Taktak, Khouloud Boukadi & Faiez Gargouri

  2. Jean Moulin Lyon 3 University, CNRS, LIRIS UMR5205, University of Lyon, Lyon, France

    Hela Taktak

  3. InnoRenew CoE, University of Primorska, Koper, Slovenia

    Firas Zouari

  4. Jean Moulin Lyon 3 University, CNRS, LIRIS UMR5205, iaelyon school of Management, University of Lyon, Lyon, France

    Chirine Ghedira Guégan

  5. Jean Moulin Lyon 3 University, iaelyon school of Management, CNRS, INSA Lyon, UCBL, LIRIS, UMR5205, F-69008, University of Lyon, Lyon, France

    Michael Mrissa

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  1. Hela Taktak
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Correspondence to Hela Taktak.

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Taktak, H., Boukadi, K., Zouari, F. et al. A knowledge-driven service composition framework for wildfire prediction. Cluster Comput 27, 977–996 (2024). https://doi.org/10.1007/s10586-023-03997-w

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