A unifying framework for situation identification methodologies

O Murdoch; Patrick Nixon

A unifying framework for situation identification methodologies

O Murdoch, Patrick Nixon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Unknown Host Publication
Editors	J Cassens, A Kofold-Petersen
Publisher	CEUR Workshop Proceedings
Pages	49-60
Number of pages	12
Volume	618
Publication status	Published (in print/issue) - 2010
Event	Proceedings of the 6th International Workshop on Modeling and Reasoning in Context - Lisbon, Portugal Duration: 1 Jan 2010 → …

Workshop

Workshop	Proceedings of the 6th International Workshop on Modeling and Reasoning in Context
Period	1/01/10 → …

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title = "A unifying framework for situation identification methodologies",

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.",

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editor = "J Cassens and A Kofold-Petersen",

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note = "Proceedings of the 6th International Workshop on Modeling and Reasoning in Context ; Conference date: 01-01-2010",

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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

PB - CEUR Workshop Proceedings

T2 - Proceedings of the 6th International Workshop on Modeling and Reasoning in Context

Y2 - 1 January 2010

ER -

A unifying framework for situation identification methodologies

Abstract

Workshop

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