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Knowledge Extraction from Environmental Data Through a Cognitive Architecture

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Innovations in Hybrid Intelligent Systems

Part of the book series: Advances in Soft Computing ((AINSC,volume 44))

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Abstract

Wireless Sensor Networks represent a novel technology which is expected to experience a dramatic diffusion thanks to the promise to be a pervasive sensory means; however, one of the issues limiting their potential growth relies in the difficulty of managing and interpreting huge amounts of collected data. This paper proposes a cognitive architecture for the extraction of high-level knowledge from raw data through the representation of processed data in opportune conceptual spaces. The presented framework interposes a conceptual layer between the subsymbolic one, devoted to sensory data processing, and the symbolic one, aimed at describing the environment by means of a high level language. The features of the proposed approach are illustrated through the description of a sample application for wildfire detection.

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© 2007 Springer-Verlag Berlin Heidelberg

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Gaglio, S., Gatani, L., Lo Re, G., Ortolani, M. (2007). Knowledge Extraction from Environmental Data Through a Cognitive Architecture. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_43

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  • DOI: https://doi.org/10.1007/978-3-540-74972-1_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74971-4

  • Online ISBN: 978-3-540-74972-1

  • eBook Packages: EngineeringEngineering (R0)

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