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http://dx.doi.org/10.15207/JKCS.2018.9.10.075

Heterogeneous Lifelog Mining Model in Health Big-data Platform  

Kang, JI-Soo (Data Mining Lab., Department of Computer Science, Kyonggi University)
Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.10, 2018 , pp. 75-80 More about this Journal
Abstract
In this paper, we propose heterogeneous lifelog mining model in health big-data platform. It is an ontology-based mining model for collecting user's lifelog in real-time and providing healthcare services. The proposed method distributes heterogeneous lifelog data and processes it in real time in a cloud computing environment. The knowledge base is reconstructed by an upper ontology method suitable for the environment constructed based on the heterogeneous ontology. The restructured knowledge base generates inference rules using Jena 4.0 inference engines, and provides real-time healthcare services by rule-based inference methods. Lifelog mining constructs an analysis of hidden relationships and a predictive model for time-series bio-signal. This enables real-time healthcare services that realize preventive health services to detect changes in the users' bio-signal by exploring negative or positive correlations that are not included in the relationships or inference rules. The performance evaluation shows that the proposed heterogeneous lifelog mining model method is superior to other models with an accuracy of 0.734, a precision of 0.752.
Keywords
Ontology; Lifelog; Data Mining; Healthcare; Potential Health-risk; Cloud Computing;
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Times Cited By KSCI : 3  (Citation Analysis)
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