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A Database as a Service for the Healthcare System to Store Physiological Signal Data.

Hsien-Tsung Chang1,2, Tsai-Huei Lin1

  • 1Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.

Plos One
|December 30, 2016
PubMed
Summary
This summary is machine-generated.

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Wearable devices generate large physiological signal datasets. A novel Database as a Service (DaaS) model improves data storage and access performance for self-health management, enhancing data security.

Area of Science:

  • Biomedical Engineering
  • Data Science
  • Health Informatics

Background:

  • Wearable devices are increasingly used for self-health management, generating large volumes of physiological data.
  • These physiological records are valuable for ongoing health monitoring and medical care.
  • Challenges exist in efficiently and securely managing these large, privacy-sensitive datasets.

Purpose of the Study:

  • To address issues related to the storage and access of physiological signal records from wearable devices.
  • To leverage the Database as a Service (DaaS) model for managing large-scale physiological data.
  • To enhance data access efficiency and ensure data privacy for users of wearable health devices.

Main Methods:

  • Utilized a Database as a Service (DaaS) model tailored for physiological signal records.

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  • Implemented file pattern storage strategies to reduce database load and improve data access.
  • Integrated privacy control settings for secure data storage and access by designated users.

    • Main Results:

    • The proposed DaaS system demonstrated superior database access performance compared to traditional relational databases.
    • The experimental results showed a minimal increase in database volume.
    • The system effectively improved overall data storage performance for physiological signals.

    Conclusions:

    • The developed DaaS model offers a viable solution for managing large datasets from wearable health devices.
    • The system enhances both the efficiency of data access and the security of sensitive physiological information.
    • This approach supports the growing need for effective self-health management tools powered by wearable technology.