iCROSS: toward a scalable infrastructure for cross-domain context management

Guo, Bin; Zhang, Daqing; Sun, Lin; Yu, Zhiwen; Zhou, Xingshe

doi:10.1007/s00779-012-0564-8

Cited by 4 publications

(1 citation statement)

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“…However, this framework supported distributed administration and processing of contexts. The challenges linked to the cross-domain context management were also addressed by [ 22 ], in which authors identified context patterns in the multi-domain environment and proposed iCROSS context management infrastructure. Lin et al [ 23 ] developed a unified approach for mutual tagging of heterogeneous domains constituted by the people, their associated events at certain locations by integrating based on feature similarity and cross-domain relations.…”

Section: Related Workmentioning

confidence: 99%

mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification

Razzaq

Villalonga

Lee

et al. 2017

Sensors

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The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.

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Section: Related Workmentioning

confidence: 99%