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Pillars for Big Data and Military Health Care: State of the Art

  • Diana Martinez-MosqueraEmail author
  • Sergio Luján-Mora
  • Luis H. Montoya L.
  • Rolando P. Reyes Ch.
  • Manolo Paredes Calderón
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1066)

Abstract

Big Data is a buzzword used to describe the processing of high volumes of data. Some types of health data are considered as Big Data due to the huge amount of data originated in this sector. Researchers have consolidated their efforts to present new tools and platforms for Big Data in health care, especially with the exponential growth observed on remote sensors. Although no specific studies have been presented at the military health context, the collected experience from several reviews proves the need for applying Big Data techniques to ensure efficient military operations. In this paper, we present the attained results from state of the art studies about Big Data and health case reviews published during the 2014 to 2018 timeframe. As a result, 17 relevant studies were found from several scientific digital libraries; the main proposed approaches and methodologies that are able to be included into the military health care domain were summarized into acquisition, storage, processing, management, security, and normative pillars. The results reveal the need for further studies regarding the military health care using Big Data approaches in order to improve the military life. It is important to mention that militaries are constantly exposed to health risks and this is the main reason for monitoring their health status.

Keywords

Big Data Health care Military Review State of the art 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain
  2. 2.Departamento de Ciencias de la IngenieríaUniversidad IsraelQuitoEcuador
  3. 3.Universidad de las Fuerzas Armadas ESPESangolquíEcuador

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