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

2.50
Hdl Handle:
http://hdl.handle.net/10547/623239
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
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 health situations. However, there are many challenges for data engineering and knowledge discovery research to enable efficiently dealing with the data that are collected from heterogonous devices and applications with big volumes and velocity. This paper presents a research work in MyHealthAvatar project, which developed a comprehensive semantic driven knowledge discovery framework based on the integrated data from multiple data resources. The framework applies cloud-based hybrid database architecture of NoSQL and RDF repositories with introductions of semantic oriented data mining and knowledge lifting algorithms. The major aim of the research is to enhance the knowledge management and discovery capabilities and efficiency to support further accurate health risk analysis and lifestyle summarization.
Citation:
Yu HQ, Zhao X, Deng Z, Dong F (2017) 'Semantic lifting and reasoning on the personalised activity big data repository for healthcare research', IEEE International conference on Internet of Things 2017 - Exeter, UK, IEEE.
Publisher:
IEEE
Issue Date:
21-Jun-2017
URI:
http://hdl.handle.net/10547/623239
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
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.accessioned2019-04-15T10:47:49Z-
dc.date.available2019-04-15T10:47:49Z-
dc.date.issued2017-06-21-
dc.identifier.citationYu HQ, Zhao X, Deng Z, Dong F (2017) 'Semantic lifting and reasoning on the personalised activity big data repository for healthcare research', IEEE International conference on Internet of Things 2017 - Exeter, UK, IEEE.en
dc.identifier.doi10.1109/iThings-GreenCom-CPSCom-SmartData.2017.125-
dc.identifier.urihttp://hdl.handle.net/10547/623239-
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 health situations. However, there are many challenges for data engineering and knowledge discovery research to enable efficiently dealing with the data that are collected from heterogonous devices and applications with big volumes and velocity. This paper presents a research work in MyHealthAvatar project, which developed a comprehensive semantic driven knowledge discovery framework based on the integrated data from multiple data resources. The framework applies cloud-based hybrid database architecture of NoSQL and RDF repositories with introductions of semantic oriented data mining and knowledge lifting algorithms. The major aim of the research is to enhance the knowledge management and discovery capabilities and efficiency to support further accurate health risk analysis and lifestyle summarization.en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttps://ieeexplore.ieee.org/document/8276844en
dc.subjectsemantic weben
dc.subjectbig dataen
dc.subjecthealthcareen
dc.subjectmachine learningen
dc.subjectG700 Artificial Intelligenceen
dc.titleSemantic lifting and reasoning on the personalised activity big data repository for healthcare researchen
dc.typeConference papers, meetings and proceedingsen
dc.date.updated2019-04-15T10:45:50Z-
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