Big IoT Healthcare Data Analytics Framework Based on Fog and Cloud Computing
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초록
Throughout the world, aging populations and doctor shortages have helped drive the increasing demand forsmart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things(IoT), big data, and machine learning. However, these advances result in the generation of large amounts ofdata, making healthcare data analysis a major issue. These data have a number of complex properties such ashigh-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. Thesechallenges are met by big data analytics. In this paper, we propose an innovative analytic framework for bighealthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware betweenheterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enablesthe use of both fog computing and cloud platforms to handle the problems faced through online and offline dataprocessing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge ofpatient medical data.
Abstract
Throughout the world, aging populations and doctor shortages have helped drive the increasing demand forsmart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things(IoT), big data, and machine learning. However, these advances result in the generation of large amounts ofdata, making healthcare data analysis a major issue. These data have a number of complex properties such ashigh-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. Thesechallenges are met by big data analytics. In this paper, we propose an innovative analytic framework for bighealthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware betweenheterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enablesthe use of both fog computing and cloud platforms to handle the problems faced through online and offline dataprocessing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge ofpatient medical data.
참고문헌 (10)
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