6th International ICST Conference on Collaborative Computing: Networking, Applications, Worksharing

Research Article

Cloud-based platform for personalization in a wellness management ecosystem: Why, what, and how

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2010.44,
        author={Pei-Yun S. Hsueh and Raymund J.R. Lin and Mark J.H. Hsiao and Liangzhao Zeng and Sreeram Ramakrishnan and Henry Chang},
        title={Cloud-based platform for personalization in a wellness management ecosystem: Why, what, and how},
        proceedings={6th International ICST Conference on Collaborative Computing: Networking, Applications, Worksharing},
        publisher={IEEE},
        proceedings_a={COLLABORATECOM},
        year={2011},
        month={5},
        keywords={computer-tailored lifestyle intervention cloud computing personalization dynamic pricing active learning feature selection sample selection},
        doi={10.4108/icst.collaboratecom.2010.44}
    }
    
  • Pei-Yun S. Hsueh
    Raymund J.R. Lin
    Mark J.H. Hsiao
    Liangzhao Zeng
    Sreeram Ramakrishnan
    Henry Chang
    Year: 2011
    Cloud-based platform for personalization in a wellness management ecosystem: Why, what, and how
    COLLABORATECOM
    ICST
    DOI: 10.4108/icst.collaboratecom.2010.44
Pei-Yun S. Hsueh1, Raymund J.R. Lin2, Mark J.H. Hsiao2, Liangzhao Zeng1, Sreeram Ramakrishnan1, Henry Chang1
  • 1: IBM T.J. Watson Research Center, Hawthorne, NY 10583 USA
  • 2: IBM Taiwan Research Collaboratory, Taipei, Taiwan

Abstract

Offering personalized services through dynamically formed ecosystems is essential to personal wellness management. In this paper, we present the design of a cloud-enabled platform to facilitate the collection and delivery of evidence for personalization in a multi-provider ecosystem environment. In addition, the platform also provides essential building blocks of personalization services: smarter analytics for active personalization and dynamic provisioning. While the former common service takes charge of inferring user wellness risks from multiple data sources on the fly and making risk-driven recommendations, the latter common service determines optimal platform pricing and resource allocation given the constraint of acceptable quality of service.