mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification.
mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification.
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DOI：
10.3390/s17102433
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发表时间：
2017-10-24
期刊：
影响因子：
--
通讯作者：
Ali Khan W
中科院分区：
文献类型：
--
作者：
Razzaq MA;Villalonga C;Lee S;Akhtar U;Ali M;Kim ES;Khattak AM;Seung H;Hur T;Bang J;Kim D;Ali Khan W
在普适计算系统的跨域环境中自动识别用户上下文的新兴研究已被证明是成功的。监控多样化用户的环境和行为有助于使用环境感知应用程序控制与慢性疾病相关的生活方式。然而，跨域异构上下文的可用性为它们的融合获取抽象信息以进行进一步分析提供了具有挑战性的机会。这项工作展示了我们之前的工作从单一领域（即身体活动）扩展到多个领域（身体活动、营养和临床）以实现情境意识。我们提出了多级上下文感知框架（mlCAF），它融合了多级跨域上下文，以便仲裁更丰富的行为上下文。这项工作明确关注与基于 mlCAF 开源本体的多级上下文建模、推理和融合相关的关键挑战。更具体地说，它解决了来自三个不同领域的上下文的解释，它们的融合符合更丰富的上下文信息。本文在本体论演化方面做出了贡献，包括附加领域、上下文定义、规则和语义查询的包含。在框架评估中，使用从 20 个用户收集的多级跨领域上下文来确定抽象上下文，作为行为建模和生活方式识别的基础。实验结果表明，对于收集的跨域上下文，上下文识别的平均准确率约为 92.65%。
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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DOI：
10.3390/s140609628
发表时间：
2014-05-30
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Khattak AM;Akbar N;Aazam M;Ali T;Khan AM;Jeon S;Hwang M;Lee S
通讯作者：
Lee S
DOI：
10.1016/j.future.2013.04.004
发表时间：
2014-04-01
期刊：
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE
影响因子：
--
作者：
Bae, Ihn-Han
通讯作者：
Bae, Ihn-Han
iCROSS：面向跨域上下文管理的可扩展基础架构
影响因子：
--
作者：
Guo, Bin;Zhang, Daqing;Zhou, Xingshe
通讯作者：
Zhou, Xingshe
影响因子：
1.9
作者：
Ni, Qin;Garcia Hernando, Ana Belen;Pau de la Cruz, Ivan
通讯作者：
Pau de la Cruz, Ivan
影响因子：
35.6
作者：
Perera, Charith;Zaslavsky, Arkady;Georgakopoulos, Dimitrios
通讯作者：
Georgakopoulos, Dimitrios