A unifying framework for situation identification methodologies

O Murdoch, Patrick Nixon

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Situation identification methodologies in pervasive computing aim to abstract low level context data into more meaningful high level contexts for use by context-aware application developers and users. Many situation identification techniques have been developed and successfully applied to limited scenarios. It is not possible to apply a single technique to a wide range of diverse applications. An ideal solution would allow the combination and and interchanging of techniques as appropriate. We propose a unifying framework for existing situation identification methodologies that will automatically select the best techniques for an application.
    LanguageEnglish
    Title of host publicationUnknown Host Publication
    EditorsJ Cassens, A Kofold-Petersen
    Pages49-60
    Number of pages12
    Volume618
    Publication statusPublished - 2010
    EventProceedings of the 6th International Workshop on Modeling and Reasoning in Context - Lisbon, Portugal
    Duration: 1 Jan 2010 → …

    Workshop

    WorkshopProceedings of the 6th International Workshop on Modeling and Reasoning in Context
    Period1/01/10 → …

    Fingerprint

    Ubiquitous computing

    Keywords

    • n/a

    Cite this

    Murdoch, O., & Nixon, P. (2010). A unifying framework for situation identification methodologies. In J. Cassens, & A. Kofold-Petersen (Eds.), Unknown Host Publication (Vol. 618, pp. 49-60)
    Murdoch, O ; Nixon, Patrick. / A unifying framework for situation identification methodologies. Unknown Host Publication. editor / J Cassens ; A Kofold-Petersen. Vol. 618 2010. pp. 49-60
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    Murdoch, O & Nixon, P 2010, A unifying framework for situation identification methodologies. in J Cassens & A Kofold-Petersen (eds), Unknown Host Publication. vol. 618, pp. 49-60, Proceedings of the 6th International Workshop on Modeling and Reasoning in Context, 1/01/10.

    A unifying framework for situation identification methodologies. / Murdoch, O; Nixon, Patrick.

    Unknown Host Publication. ed. / J Cassens; A Kofold-Petersen. Vol. 618 2010. p. 49-60.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    TY - GEN

    T1 - A unifying framework for situation identification methodologies

    AU - Murdoch, O

    AU - Nixon, Patrick

    PY - 2010

    Y1 - 2010

    N2 - Situation identification methodologies in pervasive computing aim to abstract low level context data into more meaningful high level contexts for use by context-aware application developers and users. Many situation identification techniques have been developed and successfully applied to limited scenarios. It is not possible to apply a single technique to a wide range of diverse applications. An ideal solution would allow the combination and and interchanging of techniques as appropriate. We propose a unifying framework for existing situation identification methodologies that will automatically select the best techniques for an application.

    AB - Situation identification methodologies in pervasive computing aim to abstract low level context data into more meaningful high level contexts for use by context-aware application developers and users. Many situation identification techniques have been developed and successfully applied to limited scenarios. It is not possible to apply a single technique to a wide range of diverse applications. An ideal solution would allow the combination and and interchanging of techniques as appropriate. We propose a unifying framework for existing situation identification methodologies that will automatically select the best techniques for an application.

    KW - n/a

    M3 - Conference contribution

    VL - 618

    SP - 49

    EP - 60

    BT - Unknown Host Publication

    A2 - Cassens, J

    A2 - Kofold-Petersen, A

    ER -

    Murdoch O, Nixon P. A unifying framework for situation identification methodologies. In Cassens J, Kofold-Petersen A, editors, Unknown Host Publication. Vol. 618. 2010. p. 49-60