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A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare.

Emna Mezghani1,2,3, Ernesto Exposito4,5, Khalil Drira4,5

  • 1CNRS; LAAS, 7 av. du Colonel Roche, F-31400, Toulouse, France. emna.mezghani@laas.fr.

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Summary

Wearable technologies in healthcare generate vast, complex data. A new semantic big data architecture, "Knowledge as a Service," addresses these challenges for better patient monitoring and physician support.

Keywords:
Big dataData integrationHealthcareHeterogeneityScalabilityWearable computing

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Area of Science:

  • Health Informatics
  • Big Data Analytics
  • Wearable Computing

Background:

  • Wearable technologies offer advanced healthcare monitoring, transforming traditional systems.
  • Proliferation of wearable devices presents significant data management and integration challenges.
  • Data diversity, volume, and distribution complicate processing and analytics in healthcare.

Purpose of the Study:

  • To propose a generic semantic big data architecture for managing heterogeneous wearable healthcare data.
  • To address scalability and data heterogeneity issues in wearable health monitoring.
  • To enhance data understanding and information generation through semantic enrichment.

Main Methods:

  • Developed a "Knowledge as a Service" (KaaS) approach.
  • Enriched the NIST Big Data model with semantic capabilities.
  • Implemented and evaluated a Wearable KaaS platform.

Main Results:

  • Successfully managed heterogeneous data from multiple wearable devices.
  • Enabled smart understanding and correlation of scattered medical data.
  • Generated accurate and valuable information for physician supervision.

Conclusions:

  • The proposed semantic big data architecture effectively handles wearable data challenges.
  • The Wearable KaaS platform assists physicians in patient health supervision.
  • Improved patient status updates and early-stage care management are facilitated.