Development of context-aware applications is inherently complex. These applications adapt to changing context information: physical context, computational context, and user context/tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure prone. The pervasive computing community increasingly understands that developing context-aware applications should be supported by adequate context information modelling and reasoning techniques. These techniques reduce the complexity of context-aware applications and improve their maintainability and evolvability. In this paper we discuss the requirements that context modelling and reasoning techniques should meet, including the modelling of a variety of context information types and their relationships, of high-level context abstractions describing real world situations using context information facts, of histories of context information, and of uncertainty of context information. This discussion is followed by a description and comparison of current context modelling and reasoning techniques and a lesson learned from this comparison.
A survey of context modeling and reasoning techniques / C. Bettini, O. Brdiczka, K. Henricksen, J. Indulska, D. Nicklas, A. Ranganathan, D. Riboni. - In: PERVASIVE AND MOBILE COMPUTING. - ISSN 1574-1192. - 6:2(2010 Apr), pp. 161-180. [10.1016/j.pmcj.2009.06.002]
A survey of context modeling and reasoning techniques
C. BettiniPrimo
;D. RiboniUltimo
2010
Abstract
Development of context-aware applications is inherently complex. These applications adapt to changing context information: physical context, computational context, and user context/tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure prone. The pervasive computing community increasingly understands that developing context-aware applications should be supported by adequate context information modelling and reasoning techniques. These techniques reduce the complexity of context-aware applications and improve their maintainability and evolvability. In this paper we discuss the requirements that context modelling and reasoning techniques should meet, including the modelling of a variety of context information types and their relationships, of high-level context abstractions describing real world situations using context information facts, of histories of context information, and of uncertainty of context information. This discussion is followed by a description and comparison of current context modelling and reasoning techniques and a lesson learned from this comparison.Pubblicazioni consigliate
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