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http://dx.doi.org/10.3745/KTSDE.2018.7.2.43

Bio-Sensing Convergence Big Data Computing Architecture  

Ko, Myung-Sook (부천대학교 경영과)
Lee, Tae-Gyu (원광대학교 바이오나노화학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.7, no.2, 2018 , pp. 43-50 More about this Journal
Abstract
Biometric information computing is greatly influencing both a computing system and Big-data system based on the bio-information system that combines bio-signal sensors and bio-information processing. Unlike conventional data formats such as text, images, and videos, biometric information is represented by text-based values that give meaning to a bio-signal, important event moments are stored in an image format, a complex data format such as a video format is constructed for data prediction and analysis through time series analysis. Such a complex data structure may be separately requested by text, image, video format depending on characteristics of data required by individual biometric information application services, or may request complex data formats simultaneously depending on the situation. Since previous bio-information processing computing systems depend on conventional computing component, computing structure, and data processing method, they have many inefficiencies in terms of data processing performance, transmission capability, storage efficiency, and system safety. In this study, we propose an improved biosensing converged big data computing architecture to build a platform that supports biometric information processing computing effectively. The proposed architecture effectively supports data storage and transmission efficiency, computing performance, and system stability. And, it can lay the foundation for system implementation and biometric information service optimization optimized for future biometric information computing.
Keywords
Bio-Sensing; Wearable Device; Digital Patch; Big Data; Hadoop; Dynamic Process;
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Times Cited By KSCI : 1  (Citation Analysis)
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