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        Human-Centered Services Computing for Smart Cities

        IEICE Monograph

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        Contributor(s)
        Murakami, Yohei (editor)
        Kimura, Kosaku (editor)
        Language
        English
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        Abstract
        This open-access book compiles seven monographs from researchers at the forefront of services computing and artificial intelligence for smart cities. This is structured into three thematic parts: service composition, big data analysis, and service integration for smart cities. The first part describes service compositions for smart cities, where interaction between services and the physical world, including humans, is paramount, unlike services on the Web and clouds. The second part addresses the challenges of big data analytics in smart cities, with a focus on privacy-preserving methodologies. The third part reports human-centered service integration for applications in smart cities.
        URI
        https://library.oapen.org/handle/20.500.12657/90470
        Keywords
        Service Computing; Service Composition; Big Data Analysis; Privacy-Preserving Data Mining; Multi-Agent Systems; Smart City; Human-in-the-loop; Crowdsourcing
        DOI
        10.1007/978-981-97-0779-9
        ISBN
        9789819707799, 9789819707799, 9789819707782
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Singapore, 2024
        Grantor
        • Japan Society for the Promotion of Science - [...]
        • Japan Science and Technology Agency - [...]
        • University of Aizu - [...]
        Imprint
        Springer Nature Singapore
        Classification
        Artificial intelligence
        Pages
        309
        Rights
        http://creativecommons.org/licenses/by-nc-nd/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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