Semantic lifting and reasoning on the personalised activity big data repository for healthcare research

2.50
Hdl Handle:
http://hdl.handle.net/10547/623513
Title:
Semantic lifting and reasoning on the personalised activity big data repository for healthcare research
Authors:
Yu, Hong Qing; Zhao, Xia; Deng, Zhikun ( 0000-0002-3659-757X ) ; Dong, Feng ( 0000-0003-4122-8012 )
Abstract:
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:
IEEE
Issue Date:
1-Oct-2017
URI:
http://hdl.handle.net/10547/623513
DOI:
10.1109/iThings-GreenCom-CPSCom-SmartData.2017.125
Additional Links:
https://ieeexplore.ieee.org/document/8276844
Type:
Conference papers, meetings and proceedings
Language:
en
ISSN:
1476-1289
EISSN:
1741-9212
Appears in Collections:
Computing

Full metadata record

DC FieldValue Language
dc.contributor.authorYu, Hong Qingen
dc.contributor.authorZhao, Xiaen
dc.contributor.authorDeng, Zhikunen
dc.contributor.authorDong, Fengen
dc.date.accessioned2019-10-04T09:40:20Z-
dc.date.available2019-10-04T09:40:20Z-
dc.date.issued2017-10-01-
dc.identifier.citationYu 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.en
dc.identifier.issn1476-1289-
dc.identifier.doi10.1109/iThings-GreenCom-CPSCom-SmartData.2017.125-
dc.identifier.urihttp://hdl.handle.net/10547/623513-
dc.description.abstractThe 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.en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttps://ieeexplore.ieee.org/document/8276844en
dc.rightsYellow - can archive pre-print (ie pre-refereeing)-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectbig dataen
dc.subjectknowledge managementen
dc.subjectsemantic weben
dc.subjectontologyen
dc.subjectdata processingen
dc.subjecthealthcareen
dc.subjectG560 Data Managementen
dc.titleSemantic lifting and reasoning on the personalised activity big data repository for healthcare researchen
dc.typeConference papers, meetings and proceedingsen
dc.identifier.eissn1741-9212-
dc.date.updated2019-10-04T09:35:16Z-
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