Semantic lifting and reasoning on the personalised activity big data repository for healthcare research
Issue Date
2017-10-01Subjects
big dataknowledge management
semantic web
ontology
data processing
healthcare
G560 Data Management
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Show full item recordAbstract
The fast growing markets of smart health monitoring devices and mobile applications provide opportunities for common citizens to have capability for understanding and managing their own health situations. However, there are many challenges for data engineering and knowledge discovery research to enable efficient extraction of knowledge from data that is collected from heterogonous devices and applications with big volumes and velocity. This paper presents research that initially started with the EC MyHealthAvatar project and is under continual improvement following the project’s completion. The major contribution of the work is a comprehensive big data and semantic knowledge discovery framework which integrates data from varied data resources. The framework applies hybrid database architecture of NoSQL and RDF repositories with introductions for semantic oriented data mining and knowledge lifting algorithms. The activity stream data is collected through Kafka’s big data processing component. The motivation of the research is to enhance the knowledge management, discovery capabilities and efficiency to support further accurate health risk analysis and lifestyle summarisation.Citation
Yu H, Zhao X, Deng Z, Dong F, (2017) 'Semantic lifting and reasoning on the personalised activity big data repository for healthcare research', 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) - Exeter, IEEE.Publisher
IEEEAdditional Links
https://ieeexplore.ieee.org/document/8276844Type
Conference papers, meetings and proceedingsLanguage
enISSN
1476-1289EISSN
1741-9212ae974a485f413a2113503eed53cd6c53
10.1109/iThings-GreenCom-CPSCom-SmartData.2017.125
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- Creative Commons
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