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A Hadoop/MapReduce Based Platform for Supporting Health Big Data Analytics.

Alex Kuo1, Dillon Chrimes2, Pinle Qin3

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This study details a Hadoop-based platform for health Big Data Analytics, using three billion patient records. The system demonstrated efficient data querying, highlighting its potential for health authority data management.

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Area of Science:

  • Computer Science
  • Health Informatics
  • Data Science

Background:

  • Health Big Data Analytics (BDA) platforms are crucial for managing large, complex health datasets.
  • Existing research lacks practical implementation studies of BDA platforms within health authority systems.
  • Hadoop/MapReduce offers a scalable framework for processing massive data volumes.

Purpose of the Study:

  • To design and implement a practical health Big Data Analytics platform using Hadoop/MapReduce.
  • To evaluate the platform's performance using a large-scale, emulated health dataset.
  • To address the gap in implementation studies for BDA in health authorities.

Main Methods:

  • Developed a platform utilizing the Hadoop/MapReduce framework.
  • Constructed and cross-referenced three billion emulated health records with metadata.
  • Stored patient data on a Hadoop Distributed File System to simulate a health authority's information system.
  • Benchmarked platform performance using data query tests on the WestGrid High Performance Computing platform.

Main Results:

  • Successfully designed and implemented a functional health Big Data Analytics platform.
  • Demonstrated the platform's capability to handle and query a large-scale health dataset.
  • Performance benchmarks indicated the platform's viability for health authority data analysis.

Conclusions:

  • The implemented Hadoop-based platform is a practical solution for health Big Data Analytics.
  • This study provides valuable insights into the challenges and successes of applying BDA in a real-world health authority context.
  • Further implementation studies are needed to validate and refine such platforms for widespread adoption.