A Context Modeling Survey

A Context Modeling Survey

| Thomas Strang, Claudia Linnhoff-Popien
This paper provides a comprehensive survey of current approaches to modeling context for ubiquitous computing. Context-awareness is a key driver in the paradigm of ubiquitous computing, and a well-designed model is essential for effective context handling. The authors review and evaluate various context modeling approaches, classifying them based on their core elements and assessing their suitability for ubiquitous computing environments. The paper begins with an introduction to the importance of context-awareness and the challenges it poses in distributed systems. It outlines six fundamental requirements for context modeling in ubiquitous computing: distributed composition, partial validation, richness and quality of information, incompleteness and ambiguity, level of formality, and applicability to existing environments. The main body of the paper discusses several modeling approaches, including key-value models, markup scheme models, graphical models, object-oriented models, logic-based models, and ontology-based models. Each approach is evaluated against the six fundamental requirements. Key-value models are simple but lack sophisticated structuring for efficient retrieval. Markup scheme models, such as profiles, offer hierarchical structures but may struggle with complex relationships. Graphical models, like UML, are flexible but may not handle dynamic contexts well. Object-oriented models are strong in distributed composition but can be resource-intensive. Logic-based models are highly formal but lack partial validation. Ontology-based models, particularly those using ontologies, are the most promising due to their ability to handle distributed composition, partial validation, and quality meta-information. The paper concludes that while no single approach fully meets all requirements, ontology-based models are the most suitable for ubiquitous computing environments. However, other approaches may still have applications in specific contexts. The authors also emphasize the need for further research and evaluation of emerging approaches.This paper provides a comprehensive survey of current approaches to modeling context for ubiquitous computing. Context-awareness is a key driver in the paradigm of ubiquitous computing, and a well-designed model is essential for effective context handling. The authors review and evaluate various context modeling approaches, classifying them based on their core elements and assessing their suitability for ubiquitous computing environments. The paper begins with an introduction to the importance of context-awareness and the challenges it poses in distributed systems. It outlines six fundamental requirements for context modeling in ubiquitous computing: distributed composition, partial validation, richness and quality of information, incompleteness and ambiguity, level of formality, and applicability to existing environments. The main body of the paper discusses several modeling approaches, including key-value models, markup scheme models, graphical models, object-oriented models, logic-based models, and ontology-based models. Each approach is evaluated against the six fundamental requirements. Key-value models are simple but lack sophisticated structuring for efficient retrieval. Markup scheme models, such as profiles, offer hierarchical structures but may struggle with complex relationships. Graphical models, like UML, are flexible but may not handle dynamic contexts well. Object-oriented models are strong in distributed composition but can be resource-intensive. Logic-based models are highly formal but lack partial validation. Ontology-based models, particularly those using ontologies, are the most promising due to their ability to handle distributed composition, partial validation, and quality meta-information. The paper concludes that while no single approach fully meets all requirements, ontology-based models are the most suitable for ubiquitous computing environments. However, other approaches may still have applications in specific contexts. The authors also emphasize the need for further research and evaluation of emerging approaches.
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