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Big Health Data Resource Integration Method Based on Hybrid Cloud and Fog Computing

Published: 29 May 2020 Publication History

Abstract

Aiming at the problems of high latency and poor accuracy of retrieval response in current health data integration related research, this paper presents a health data resource integration method based on Hybrid Cloud and Fog Computing. Based on the general model of health data resource service platform, the framework of large health data resource integration is constructed. Sample reduction and dimension reduction are used to reduce big health data. The field matching method based on participle and weight is used to clean data resources and calculate the field matching degree. When the matching degree is larger than a threshold value, the fields to be matched are similar duplicate records, and the redundant data is removed. The weight of resource data is calculated by Hybrid Cloud and Fog Computing, and the big health data is arranged according to the weight value, so as to realize the classification and integration of health data.The experimental results show that the proposed method has good performance, low data redundancy, low retrieval response delay and high classification integration accuracy.

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  • (2020)An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural NetworksSensors10.3390/s2024735320:24(7353)Online publication date: 21-Dec-2020

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  1. Big Health Data Resource Integration Method Based on Hybrid Cloud and Fog Computing

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    cover image ACM Other conferences
    ICECC '20: Proceedings of the 3rd International Conference on Electronics, Communications and Control Engineering
    April 2020
    73 pages
    ISBN:9781450374996
    DOI:10.1145/3396730
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 29 May 2020

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    Author Tags

    1. Computing
    2. HealthData
    3. Hybrid Cloud and Fog
    4. Reduction
    5. ResourceIntegration

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    • (2020)An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural NetworksSensors10.3390/s2024735320:24(7353)Online publication date: 21-Dec-2020

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