TY - GEN
T1 - Tiered data integration for mobile health systems
AU - Abu-Elkheir, Mervat
AU - Ali, Najah A.Abu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - One of the most promising instantiations of the Internet of Things (IoT) are mobile health (mHealth) systems, which promise to deliver intelligent health monitoring and assisted living as well as advanced and integrated health services. To realize the full potential of these services, fragmented and heterogeneous data that is generated by different segments of the system need to be consolidated in order to support high-quality processes. This paper proposes a tiered data integration scheme for mHealth systems that works on the schema, entity, and event levels. The proposed scheme incorporates an algorithm that merges and ranks sensor streams for schema integration and event identification, and performs contextual record registration and deduplication for entity resolution. We tested the proposed integration scheme on two sets of sensor-based mHealth data related to human activity recognition. Preliminary results show that the proposed integration scheme contributes to enhancements in event identification precision compared to the classification performance of separate datasets produced within the same mHealth system.
AB - One of the most promising instantiations of the Internet of Things (IoT) are mobile health (mHealth) systems, which promise to deliver intelligent health monitoring and assisted living as well as advanced and integrated health services. To realize the full potential of these services, fragmented and heterogeneous data that is generated by different segments of the system need to be consolidated in order to support high-quality processes. This paper proposes a tiered data integration scheme for mHealth systems that works on the schema, entity, and event levels. The proposed scheme incorporates an algorithm that merges and ranks sensor streams for schema integration and event identification, and performs contextual record registration and deduplication for entity resolution. We tested the proposed integration scheme on two sets of sensor-based mHealth data related to human activity recognition. Preliminary results show that the proposed integration scheme contributes to enhancements in event identification precision compared to the classification performance of separate datasets produced within the same mHealth system.
KW - Data integration
KW - MHealth
KW - Schema integration
KW - Sensor networks
UR - http://www.scopus.com/inward/record.url?scp=84964884724&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964884724&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2014.7417815
DO - 10.1109/GLOCOM.2014.7417815
M3 - Conference contribution
AN - SCOPUS:84964884724
T3 - 2015 IEEE Global Communications Conference, GLOBECOM 2015
BT - 2015 IEEE Global Communications Conference, GLOBECOM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 58th IEEE Global Communications Conference, GLOBECOM 2015
Y2 - 6 December 2015 through 10 December 2015
ER -